@techreport{ouvrard:arxiv1905.11695,
author = {Xavier Ouvrard and Jean-Marie Le Goff and Stéphane Marchand-Maillet},
title = {The HyperBagGraph DataEdron: An Enriched Browsing Experience of Multimedia Datasets},
institution = {CoRR abs/1905.11695},
year = {2019},
     url = {https://arxiv.org/abs/1905.11695}
}
@techreport{strasser:arxiv1905.11245,
author = {Pablo Strasser and Stéphane Armand and Stéphane Marchand-Maillet and Alexandros Kalousis},
title = {Learning by stochastic serializations},
institution = {CoRR abs/1905.11245},
year = {2019},
     url = {https://arxiv.org/abs/1905.11245}
}
@techreport{brangbour:arxiv1903.04748,
author = {Etienne Brangbour and Pierrick Bruneau and Stéphane Marchand-Maillet and Renaud Hostache and Patrick Matgen and Marco Chini and Thomas Tamisier},
title = {Extracting localized information from a Twitter corpus for flood prevention},
institution = {CoRR abs/1903.04748},
year = {2019},
     url = {https://arxiv.org/abs/1903.04748}
}

@techreport{ouvrard:arxiv1809.00190,
author = {Xavier Ouvrard and Jean-Marie Le Goff and Stéphane Marchand-Maillet},
title = {Exchange-Based Diffusion in Hb-Graphs: Highlighting Complex Relationships},
institution = {CoRR abs/1809.00190},
year = {2018},
     url = {https://arxiv.org/abs/1809.00190}
}
@techreport{ouvrard:arxiv1809.00164,
author = {Xavier Ouvrard and Jean-Marie Le Goff and Stéphane Marchand-Maillet},
title = {Hypergraph Modeling and Visualisation of Complex Co-occurence Networks},
institution = {CoRR abs/1809.00164},
year = {2018},
     url = {https://arxiv.org/abs/1809.00164}
}
@techreport{ouvrard:arxiv1809.00162,
author = {Xavier Ouvrard and Jean-Marie Le Goff and Stéphane Marchand-Maillet},
title = {On Adjacency and e-Adjacency in General Hypergraphs: Towards a New e-Adjacency Tensor},
institution = {CoRR abs/1809.00162},
year = {2018},
     url = {https://arxiv.org/abs/1809.00162}
}
@techreport{gregorova:arxiv1805.06258,
author = {Magda Gregorová and Alexandros Kalousis and Stéphane Marchand-Maillet},
title = {Structured nonlinear variable selection},
institution = {CoRR abs/1805.06258},
year = {2018},
     url = {https://arxiv.org/abs/1805.06258}
}
@techreport{gregorova:arxiv1804.07169,
author = {Magda Gregorová and Jason Ramapuram and Alexandros Kalousis and Stéphane Marchand-Maillet},
title = {Large-scale Nonlinear Variable Selection via Kernel Random Features.},
institution = {CoRR abs/1804.07169},
year = {2018},
     url = {https://arxiv.org/abs/1804.07169}
}
@techreport{ouvrard2017:arxiv1712.08189,
author = {Xavier Ouvrard and S. Marchand-Maillet},
title = {Adjacency Matrix and Co-occurrence Tensor of General Hypergraphs: Two Well Separated Notions},
institution = {CoRR abs/1712.08189},
year = {2017},
     url = {https://arxiv.org/abs/1712.08189}
}
 @techreport{nielsen2017:arxiv1701.03916,
author = {Frank Nielsen and Ke Sun and S. Marchand-Maillet},
title = {On Hölder projective divergences},
institution = {CoRR abs/1701.03916},
year = {2017},
     url = {https://arxiv.org/abs/1701.03916}
}

@techreport{ouvrard2017:arxiv1707.0015,
author = {Xavier Ouvrard and Jean-Marie Le Goff and S. Marchand-Maillet},
title = {Networks of Collaborations: Hypergraph modeling and visualisation},
institution = {CoRR abs/1707.0015},
year = {2017},
     url = {https://arxiv.org/abs/1707.0015}
}

@techreport{gregorova2015:arxiv1706.08811,
author = {Magda Gregorova and Alexandros Kalousis and S. Marchand-Maillet},
title = {Forecasting and Granger modelling with non-linear dynamical dependencies},
institution = {CoRR abs/1706.08811},
year = {2017},
     url = {https://arxiv.org/abs/1706.08811}
}
@techreport{gregorova2015:arxiv1507.01978,
author = {Magda Gregorova and Alexandros Kalousis and S. Marchand-Maillet},
title = {Learning Leading Indicators for time series predictions},
institution = {CoRR abs/1507.01978},
year = {2015},
     url = {https://arxiv.org/abs/1507.01978}
}
@techreport{wang2014:arxiv1405.2798,
author = {Jun Wang and Ke Sun and Fei Sha and S. Marchand-Maillet and Alexandros Kalousis},
title = {Two-stage Metric Learning},
institution = {CoRR abs/1405.2798},
year = {2014},
     url = {https://arxiv.org/abs/1405.2798}
}
@techreport{szekely2009:tr0901,
author = {Eniko Szekely and Eric Bruno and S. Marchand-Maillet},
title = {Collection guiding: review of the main strategies fro multimedia collection browsing},
institution = {Viper group - CVMLab, Dept of Computer Science, University of Geneva},
year = {2009},
number = {09.01},
address = {Route de Drize, 7, CH-1227, Carouge, Switzerland},
     url = {http://viper.unige.ch/documents/pdf/szekely2009-tr0901.pdf}
}
@techreport{szekely2008:tr0801,
author = {Eniko Szekely and Eric Bruno and S. Marchand-Maillet},
title = {    Unsupervised Dimension Reduction of High-Dimensional Data for Cluster Preservation},
institution = {Viper group - CVMLab, Dept of Computer Science, University of Geneva},
year = {2008},
number = {08.01},
address = {Route de Drize, 7, CH-1227, Carouge, Switzerland},
     url = {http://viper.unige.ch/documents/pdf/szekely2008-tr0801.pdf}
}
@techreport{kosinov2006:tr0601,
author = {Serhiy Kosinov and S. Marchand-Maillet},
title = {Dual diffusion model of spreading activation for content-based image
	retrieval},
institution = {Computer Vision and Multimedia Laboratory, Computing Centre, University
	of Geneva},
year = {2006},
number = {06.01},
address = {Rue G\'en\'eral Dufour, 24, CH-1211, Geneva, Switzerland},
     url = {http://viper.unige.ch/documents/pdf/kosinov2006-tr0601.pdf}
}
@techreport{VG:Oct0305,
author = {Carlo Jelmini and St{\'e}phane Marchand-Maillet},
title = {The Semantic Web Knowledge Base ({SWKB}), an {OWL} reasoning toolwith retractable inference},
institution = {Computer Vision and Multimedia Laboratory, Computing Centre, Universityof Geneva},
year = 2003,
address = {Rue G\'en\'eral Dufour, 24, CH-1211, Geneva, Switzerland}
}
@techreport{VG:Mar0303,
author = {Tayeb Bouzerda and St{\'e}phane Marchand-Maillet},
title = {A flexible framework for the development of XML protocols: Applications
	to MRML},
institution = {Computer Vision and Multimedia Laboratory, Computing Centre, University
	of Geneva},
year = {2003},
number = {03.03},
address = {rue General Dufour, 24, CH-1211, Geneva, Switzerland},
month = {June},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:Dec0307,
author = {Serhiy Kosinov},
title = {Visual object recognition using distance-based discriminant analysis},
institution = {Computer Vision and Multimedia Laboratory, Computing Centre, University
	of Geneva},
year = {2003},
number = {03.07},
address = {Rue G\'en\'eral Dufour, 24, CH-1211, Geneva, Switzerland},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:Dec0308,
author = {St{\'e}phane Marchand-Maillet},
title = {Collection guiding},
institution = {Computer Vision and Multimedia Laboratory, Computing Centre, University of Geneva},
year = 2003,
address = {Rue G\'en\'eral Dufour, 24, CH-1211, Geneva, Switzerland},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:Mar0301,
author = {St{\'e}phane Marchand-Maillet},
title = {Meeting Record Modelling for Enhanced Browsing},
institution = {Computer Vision and Multimedia Laboratory, Computing Centre, University
	of Geneva},
year = {2003},
number = {03.01},
address = {rue General Dufour, 24, CH-1211, Geneva, Switzerland},
month = {March},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:Mar0302,
author = {St{\'e}phane Marchand-Maillet},
title = {MRML: Steps towards version 2},
institution = {Computer Vision and Multimedia Laboratory, Computing Centre, University
	of Geneva},
year = {2003},
number = {03.02},
address = {rue General Dufour, 24, CH-1211, Geneva, Switzerland},
month = {March},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:Oct0304,
author = {Nicolas Mo{\"e}nne-Loccoz},
title = {Characterizing activity in video shots based on salient points},
institution = {Computer Vision and Multimedia Laboratory, Computing Centre, University
	of Geneva},
year = {2003},
number = {03.04},
address = {rue General Dufour, 24, CH-1211, Geneva, Switzerland},
month = {October},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:MMM2001,
author = {Henning M{\"u}ller and Wolfgang M{\"u}ller and St{\'e}phane Marchand-Maillet
	and David McG. Squire and Thierry Pun},
title = {Automated benchmarking in content-based image retrieval},
institution = {University of Geneva},
year = {2001},
number = {01.01},
month = {May},
url = {http://vision.unige.ch/publications/postscript/2001/MuellerHMuellerWMarchandSquirePun_tr01.pdf},
abstract = {Benchmarking has always been a crucial problem in content-based image
	retrieval (CBIR). A key issue is the lack of a common access method
	to retrieval systems, such as SQL for relational databases. The Multimedia
	Retrieval Mark-up Language (MRML) solves this problem by standardizing
	access to CBIR systems (CBIRSs). Other difficult problems are also
	shortly addressed, such as obtaining relevance judgments and choosing
	a database for performance comparison. In this article we present
	a fully automated benchmark for CBIRSs based on MRML, which can be
	adapted to any image database and almost any kind of relevance judgment.
	The test evaluates the performance of positive and negative relevance
	feedback, which can be generated automatically from the relevance
	judgments. To illustrate our purpose, a freely available, non-copyright
	image collection is used to evaluate our CBIRS, \emph{Viper}. All
	scripts described here are also freely available for download.},
url1 = {http://vision.unige.ch/publications/postscript/2001/MuellerHMuellerWMarchandSquirePun_tr01.ps.gz},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:MMM2000,
author = {Wolfgang M{\"u}ller and Henning M{\"u}ller and St{\'e}phane Marchand-Maillet
	and Thierry Pun and David McG. Squire and Zoran Pe\u{c}enovi\'{c}
	and Christoph Giess and Arjen P. de Vries},
title = {{MRML}: A Communication Protocol for Content-Based Image Retrieval},
institution = {Computer Vision Group, Computing Centre, University of Geneva},
year = {2000},
number = {00.02},
address = {rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland},
month = {mar},
url = {http://vision.unige.ch/publications/postscript/2000/VGTR00.02_MuellerWMuellerHMarchandPunSquireGiessVries.ps.gz},
abstract = {In this paper we introduce and describe the Multimedia Retrieval Markup
	Language (MRML). This XML-based markup language is the basis for
	an open communication protocol for content-based image retrieval
	systems (CBIRSs). MRML was initially designed as a means of separating
	CBIR engines from their user interfaces. It is, however, also extensible
	as the basis for standardized performance evaluation procedures.
	Such a tool is essential for the formulation and implementation of
	common benchmarks for CBIR. A common protocol can also bring new
	dynamics to the CBIR field---it makes the development of new systems
	faster and more efficient, and opens the door of the CBIR research
	field to other disciplines such as Human-Computer Interaction. The
	MRML specifications, as well as the first MRML-compliant applications,
	are freely available and are introduced in this paper.},
url1 = {http://vision.unige.ch/publications/postscript/2000/VGTR00.02_MuellerWMuellerHMarchandPunSquireGiessVries.pdf},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:MMS2000a,
author = {Henning M{\"u}ller and Wolfgang M{\"u}ller and David McG. Squire
	and Zoran Pe\u{c}enovi\'{c} and St{\'e}phane Marchand-Maillet and
	Thierry Pun},
title = {An Open Framework for Distributed Multimedia Retrieval},
institution = {Computer Vision Group, Computing Centre, University of Geneva},
year = {2000},
number = {00.03},
address = {rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland},
month = {mar},
url = {http://vision.unige.ch/publications/postscript/2000/VGTR00.03_MuellerHMuellerWSquirePecenovicMarchandPun.ps.gz},
abstract = {This article describes a framework for distributed multimedia retrieval
	which permits the connection of compliant user interfaces with a
	variety of multimedia retrieval engines via an open communication
	protocol, MRML (Multi Media Retrieval Markup Language). It allows
	the choice of image collection, feature set and query algorithm during
	run--time, permitting multiple users to query a system adapted to
	their needs, using the query paradigm adapted to their problem such
	as query by example (QBE), browsing queries, or query by annotation.
	User interaction is implemented over several levels and in diverse
	ways. Relevance feedback is implemented using positive and negative
	example images that can be used for a best--match QBE query. In contrast,
	browsing methods try to ap proach the searched image by giving overviews
	of the entire collection and by successive refinements. In addition
	to these query methods, Long term off line learning is implemented.
	It allows feature preferences per user, user domain or over all users
	to be learned automatically. We present the Viper multimedia retrieval
	system as the core of the framework and an example of an MRML-compliant
	search engine. Viper uses techniques adapted from traditional information
	retrieval (IR) to retrieve multimedia documents, thus benefiting
	from the many years of IR research. As a result, textual and visual
	features are treated in the same way, facilitating true multimedia
	retrieval. The MRML protocol also allows other applications to make
	use of the search engi nes. This can for example be used for the
	design of a benchmark test suite, querying several search engines
	in the same way and comparing the results. This is motivated by the
	fact that the content--based image retrieval community really lacks
	such a benchmark as it already exists in text retrieval.},
url1 = {http://vision.unige.ch/publications/postscript/2000/VGTR00.03_MuellerHMuellerWSquirePecenovicMarchandPun.pdf},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:MMS2000b,
author = {Henning M{\"u}ller and Wolfgang M{\"u}ller and David McG. Squire
	and St{\'e}phane Marchand-Maillet and Thierry Pun},
title = {Long-Term Learning from User Behavior in Content-Based Image Retrieval},
institution = {Computer Vision Group, Computing Centre, University of Geneva},
year = {2000},
number = {00.04},
address = {rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland},
month = {mar},
url = {http://vision.unige.ch/publications/postscript/2000/VGTR00.04_MuellerHMuellerWSquireMarchandPun.ps.gz},
abstract = {This article describes a simple algorithm for obtaining knowledge
	about the importance of features from analyzing user log files of
	a content-based image retrieval system (CBIRS). The user log files
	of the usage of the Viper web demonstration system are analyzed over
	a period of four months. In this time about 3500 accesses to the
	system were made with 800 multiple image queries. The analysis only
	takes into account multiple image queries of the system with positive
	or negative input images, because only these queries contain enough
	information for the method described in the paper. Features frequently
	present in images marked together positively in the same query step
	get a higher weighting whereas features present in an image marked
	positively and another image marked negatively in the same step get
	a lower weighting. The Viper system offers a very large number of
	simple features which allows the creation of feature weightings with
	high values for important and low values for less important features.
	These weightings for features can of course differ for several collections
	and as well for several users. The results are evaluated using the
	relevance judgments of real users and compared to the system without
	the long-term learning.},
url1 = {http://vision.unige.ch/publications/postscript/2000/VGTR00.04_MuellerHMuellerWSquireMarchandPun.pdf},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:MMS2000,
author = {Henning M{\"u}ller and Wolfgang M{\"u}ller and David McG. Squire
	and St{\'e}phane Marchand-Maillet and Thierry Pun},
title = {Strategies for positive and negative relevance feedback in image
	retrieval},
institution = {Computer Vision Group, Computing Centre, University of Geneva},
year = {2000},
number = {00.01},
address = {rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland},
month = {jan},
url = {http://vision.unige.ch/publications/postscript/2000/VGTR00.01_HMuellerWMuellerSquireMarchandPun.ps},
abstract = {Relevance feedback has been shown to be a very effective tool for
	enhancing retrieval results in text retrieval. In content-based image
	retrieval it is more and more frequently used and very good results
	have been obtained. However, too much negative feedback may destroy
	a query as good features get negative weightings. This paper compares
	a variety of strategies for positive and negative feedback. The performance
	evaluation of feedback algorithms is a hard problem. To solve this,
	we obtain judgments from several users and employ an automated feedback
	scheme. We can then evaluate different techniques using the same
	judgments. Using automated feedback, the ability of a system to adapt
	to the user's needs can be measured very effectively. Our study highlights
	the utility of negative feedback, especially over several feedback
	steps.},
url1 = {http://vision.unige.ch/publications/postscript/2000/VGTR00.01_HMuellerWMuellerSquireMarchandPun.pdf},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:Mar2000,
author = {St{\'e}phane Marchand-Maillet},
title = {Content-Based Video Retrieval: An Overview},
institution = {CUI - University of Geneva},
year = {2000},
number = {00.06},
address = {Geneva, Switzerland},
url = {http://vision.unige.ch/publications/postscript/2000/VGTR00.06_Marchand.pdf},
abstract = {Content-based Image Retrieval systems (CBIRS) start flourishing on
	the Web. Their performances are continuously improving and their
	base principles span a wide range of diversity. Content-based Video
	Retrieval systems (CBVRS) are less common and seem at a first glance
	to be a natural extension of CBIRS. In this document, we summarise
	advances made in the development of CBVRS and analyse their relationship
	to CBIRS. While doing so, we show that CBVRS are actually not so
	obvious extensions of CBIRS. (40 References)},
url1 = {http://vision.unige.ch/publications/postscript/2000/VGTR00.06_Marchand.pdf},
url2 = {http://viper.unige.ch/~marchand/CBVR/},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:MMS1999,
author = {Henning M{\"u}ller and Wolfgang M{\"u}ller and David McG. Squire
	and Thierry Pun},
title = {Performance Evaluation in Content-Based Image Retrieval: Overview
	and Proposals},
institution = {Computer Vision Group, Computing Centre, University of Geneva},
year = {1999},
number = {99.05},
address = {rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland},
month = {dec},
url = {http://vision.unige.ch/publications/postscript/99/VGTR99.05_HMuellerWMuellerSquirePun.ps.gz},
abstract = {Evaluation of retrieval performance is a crucial problem in content-based
	image retrieval (CBIR). Many different methods for measuring the
	performance of a system have been created and used by researchers.
	This article discusses the advantages and shortcomings of the performance
	measures currently used. Problems such as a common image database
	for performance comparisons and a means of getting relevance judgments
	(or ground truth) for queries are explained. The relationship between
	CBIR and information retrieval (IR) is made clear, since IR researchers
	have decades of experience with the evaluation problem. Many of their
	solutions can be used for CBIR, despite the differences between the
	fields. Several methods used in text retrieval are explained. Proposals
	for performance measures and means of developing a standard test
	suite for CBIR, similar to that used in IR at the annual Text REtrieval
	Conference (TREC), are presented.},
url1 = {http://vision.unige.ch/publications/postscript/99/VGTR99.05_HMuellerWMuellerSquirePun.pdf},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:MSM1999,
author = {Henning M{\"u}ller and David McG. Squire and Wolfgang M{\"u}ller
	and Thierry Pun},
title = {Efficient access methods for content-based image retrieval with inverted
	files},
institution = {Computer Vision Group, Computing Centre, University of Geneva},
year = {1999},
number = {99.02},
address = {rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland},
month = {July},
url = {http://vision.unige.ch/publications/postscript/99/VGTR99.02_MuellerSquireMuellerPun.ps.gz},
abstract = {As human factor studies over the last thirty years have shown, response
	time is a very important factor for the usability of an interactive
	system, especially on the world wide web. In particular, response
	times of under one second are often specified as a usability requirement
	\cite{Nie97}. This paper compares several methods for improving the
	evaluation time in a content-based image retrieval system (CBIRS)
	which uses inverted file technology. The use of the inverted file
	technology facilitates search pruning in a variety of ways, as is
	shown in this paper. For large databases ($> 2000$ images) and a
	high number of possible features ($> 80000$), efficient and fast
	access is necessary to allow interactive querying and browsing. Parallel
	access to the inverted file can reduce the response time. This parallel
	access is very easy to implement with little communication overhead,
	and thus scales well. Other search pruning methods, similar to methods
	used in information retrieval, can also reduce the response time
	significantly without reducing the performance of the system. The
	performance of the system is evaluated using precision vs. recall
	graphs, which are an established evaluation method in information
	retrieval. A user survey was carried out in order to obtain relevance
	judgments for the queries reported in this work.},
keywords = {inverted file, content-based image retrieval, efficient access, search
	pruning, speed evaluation},
url1 = {http://vision.unige.ch/publications/postscript/99/VGTR99.02_MuellerSquireMuellerPun.pdf},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:MPV1999,
author = {Wolfgang M{\"u}ller and Zoran Pe\u{c}enovi\'{c} and Arjen P. de Vries
	and David McG. Squire and Henning M{\"u}ller and Thierry Pun},
title = {{MRML}: Towards an extensible standard for multimedia querying and
	benchmarking (Draft Proposal)},
institution = {Computer Vision Group, Computing Centre, University of Geneva},
year = {1999},
number = {99.04},
address = {rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland},
month = {October},
url = {http://vision.unige.ch/publications/postscript/99/VGTR99.04_WMuellerPecenovicdeVriesSquireHMuellerPun.ps.gz},
url1 = {http://vision.unige.ch/publications/postscript/99/VGTR99.04_WMuellerPecenovicdeVriesSquireHMuellerPun.pdf},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:MSM1999a,
author = {Wolfgang M{\"u}ller and David McG. Squire and Henning M{\"u}ller
	and Thierry Pun},
title = {Hunting moving targets: an extension to {B}ayesian methods in multimedia
	databases},
institution = {Computer Vision Group, Computing Centre, University of Geneva},
year = {1999},
number = {99.03},
address = {rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland},
month = {July},
url = {http://vision.unige.ch/publications/postscript/99/VGTR99.03_MuellerSquireMuellerPun.ps.gz},
abstract = {It has been widely recognised that the difference between the level
	of abstraction of the formulation of a query (by example) and that
	of the desired result (usually an image with certain semantics) calls
	for the use of learning methods that try to bridge this gap. Cox
	\emph{et al.}~have proposed a Bayesian method to learn the user's
	preferences during each query. Cox \emph{et al.}\'s system, \texttt{PicHunter},
	is designed for optimal performance when the user is searching for
	a fixed target image. The performance of the system was evaluated
	using target testing, which ranks systems according to the number
	of interaction steps required to find the target, leading to simple,
	easily reproducible experiments. There are some aspects of image
	retrieval, however, which are not captured by this measure. In particular,
	the possibility of query drift (i.e.~a moving target) is completely
	ignored. The algorithm proposed by Cox \emph{et al.}~does not cope
	well with a change of target at a late query stage, because it is
	assumed that user feedback is noisy, but consistent. In the case
	of a moving target, however, the feedback is noisy \emph{and} inconsistent
	with earlier feedback. In this paper we propose an enhanced Bayesian
	scheme which selectively forgets inconsistent user feedback, thus
	enabling both the program and the user to ``change their minds''.
	The effectiveness of this scheme is demonstrated in moving target
	tests on a database of heterogeneous real-world images.},
keywords = {relevance feedback, query drift, target testing, Bayesian methods,
	user modelling},
url1 = {http://vision.unige.ch/publications/postscript/99/VGTR99.03_MuellerSquireMuellerPun.pdf},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:SMM1999b,
author = {David McG. Squire and Henning M{\"u}ller and Wolfgang M{\"u}ller},
title = {Improving Response Time by Search Pruning in a Content-Based Image
	Retrieval System, Using Inverted File Techniques},
institution = {Computer Vision Group, Computing Centre, University of Geneva},
year = {1999},
number = {99.01},
address = {rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland},
month = {February},
url = {http://vision.unige.ch/publications/postscript/99/VGTR99.01_SquireMuellerMueller.ps.gz},
abstract = {This paper describes several methods for improving query evaluation
	speed in a content-based image retrieval system (CBIRS). Response
	time is an extremely important factor in determining the usefulness
	of any interactive system, as has been demonstrated by human factors
	studies over the past thirty years. In particular, response times
	of less than one second are often specified as a usability requirement.
	It is shown that the use of inverted files facilitates the reduction
	of query evaluation time without significantly reducing the accuracy
	of the response. The performance of the system is evaluated using
	precision \vs recall graphs, which are an established evaluation
	method in information retrieval (IR), and are beginning to be used
	by CBIR researchers.},
keywords = {content-based image retrieval, search pruning, inverted file, response
	time},
url1 = {http://vision.unige.ch/publications/postscript/99/VGTR99.01_SquireMuellerMueller.pdf},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:Squ1998,
author = {David McG. Squire},
title = {Generalization performance of factor analysis techniques used for
	image database organization},
institution = {Computer Vision Group, Computing Centre, University of Geneva},
year = {1998},
number = {98.01},
address = {rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland},
month = {January},
url = {http://vision.unige.ch/publications/postscript/98/VGTR98.01_Squire.ps.gz},
abstract = {The goal of this paper is to evaluate the generalization performance
	of a variety of factor analysis techniques in an image database environment.
	Factor analysis techniques, such as Principal Components Analysis,
	have been proposed as means of reducing the dimensionality of the
	data stored in image retrieval systems. These techniques compute
	a transformation which is applied to vectors of image features to
	produce vectors of lower dimensionality which still characterize
	the original data well. Computing such transformations for very large
	numbers of images is computationally expensive, especially if this
	calculation must be repeated each time new images are added to the
	database. It is to be hoped, therefore, that a transformation computed
	using a subset of all possible images will perform well when applied
	to images not used in its derivation. To evaluate this generalization
	ability, we measure the agreement between partitionings of image
	sets computed using such transformations with those produced by human
	subjects.},
url1 = {http://vision.unige.ch/publications/postscript/98/VGTR98.01_Squire.pdf},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:Squ1998a,
author = {David McG. Squire},
title = {Learning a similarity-based distance measure for image database organization
	from human partitionings of an image set},
institution = {Computer Vision Group, Computing Centre, University of Geneva},
year = {1998},
number = {98.03},
address = {rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland},
month = {April},
url = {http://vision.unige.ch/publications/postscript/98/VGTR98.03_Squire.ps.gz},
abstract = {In this paper we employ human judgments of image similarity to improve
	the organization of an image database. We first derive a statistic,
	$\kappa_B$ which measures the agreement between two partitionings
	of an image set. $\kappa_B$ is used to assess agreement both amongst
	and between human and machine partitionings. This provides a rigorous
	means of choosing between competing image database organization systems,
	and of assessing the performance of such systems with respect to
	human judgments. Human partitionings of an image set are used to
	define an similarity value based on the frequency with which images
	are judged to be similar. When this measure is used to partition
	an image set using a clustering technique, the resultant partitioning
	agrees better with human partitionings than any of the feature-space-based
	techniques investigated. Finally, we investigate the use multilayer
	perceptrons and a \emph{Distance Learning Network} to learn a mapping
	from feature space to this perceptual similarity space. The Distance
	Learning Network is shown to learn a mapping which results in partitionings
	in excellent agreement with those produced by human subjects.},
url1 = {http://vision.unige.ch/publications/postscript/98/VGTR98.03_Squire.pdf},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:SMM1998a,
author = {David McG. Squire and Wolfgang M{\"u}ller and Henning M{\"u}ller},
title = {Relevance feedback and term weighting schemes for content-based image
	retrieval},
institution = {Computer Vision Group, Computing Centre, University of Geneva},
year = {1998},
number = {98.05},
address = {rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland},
month = {December},
url = {http://vision.unige.ch/publications/postscript/98/VGTR98.05_SquireMuellerMueller.ps.gz},
abstract = {This paper describes the application of techniques derived from text
	retrieval research to the content-based querying of image databases.
	Specifically, the use of inverted files, frequency-based weights
	and relevance feedback are investigated. The use of inverted files
	allows very large numbers ($\geq \mathcal{O}(104)$) of \emph{possible}
	features to be used. since search is limited to the subspace spanned
	by the features present in the query image(s). A variety of weighting
	schemes used in text retrieval are employed, yielding different results.
	We suggest possibles modifications for their use with image databases.
	The use of relevance feedback was shown to improve the query results
	significantly, as measured by precision and recall, for all users.},
url1 = {http://vision.unige.ch/publications/postscript/98/VGTR98.05_SquireMuellerMueller.pdf},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:SMM1998,
author = {David McG. Squire and Wolfgang M{\"u}ller and Henning M{\"u}ller
	and Jilali Raki},
title = {Content-based query of image databases, inspirations from text retrieval:
	inverted files, frequency-based weights and relevance feedback},
institution = {Computer Vision Group, Computing Centre, University of Geneva},
year = {1998},
number = {98.04},
address = {rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland},
month = {November},
url = {http://vision.unige.ch/publications/postscript/98/VGTR98.04_SquireMuellerMuellerRaki.ps.gz},
abstract = {In this paper we report the application of techniques inspired by
	text retrieval research to the content-based query of image databases.
	In particular, we show how the use of an inverted file data structure
	permits the use of a feature space of $\mathcal{O}(104)$ dimensions,
	by restricting search to the subspace spanned by the features present
	in the query. A suitably sparse set of colour and texture features
	is proposed. A scheme based on the frequency of occurrence of features
	in both individual images and in the whole collection provides a
	means of weighting possibly incommensurate features in a compatible
	manner, and naturally extends to incorporate relevance feedback queries.
	The use of relevance feedback is shown consistently to improve system
	performance, as measured by precision and recall.},
url1 = {http://vision.unige.ch/publications/postscript/98/VGTR98.04_SquireMuellerMuellerRaki.pdf},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:SqC1997,
author = {David McG. Squire and Terry M. Caelli},
title = {Invariance Signatures: Characterizing contours by their departures
	from invariance},
institution = {Computer Vision Group, Computing Centre, University of Geneva},
year = {1997},
number = {97.04},
address = {rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland},
month = {April},
url = {http://vision.unige.ch/publications/postscript/97/VGTR97.04_SquireCaelli.ps.gz},
url1 = {http://vision.unige.ch/publications/postscript/97/VGTR97.04_SquireCaelli.pdf},
vgclass = {report},
vgproject = {unspecified}
}
@techreport{VG:SqP1997a,
author = {David McG. Squire and Thierry Pun},
title = {Assessing Agreement Between Human and Machine Clusterings of Image
	Databases},
institution = {Computer Vision Group, Computing Centre, University of Geneva},
year = {1997},
number = {97.03},
address = {rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland},
month = {April},
url = {http://vision.unige.ch/publications/postscript/97/VGTR97.03_SquirePun.ps.gz},
abstract = {There is currently much interest in the organization and \emph{content-based}
	querying image databases. The usual hypothesis is that image similarity
	can be characterized by low-level features, without further abstraction.
	This assumes that agreement between machine and human measures of
	similarity is sufficient for the database to be useful. To assess
	this assumption, we develop measures of the agreement between partitionings
	of an image set, showing that chance agreements \emph{must} be considered.
	These measures are used to assess the agreement between human subjects
	and several machine clustering techniques on an image set. The results
	can be used to select and refine distance measures for querying and
	organizing image databases.},
url1 = {http://vision.unige.ch/publications/postscript/97/VGTR97.03_SquirePun.pdf},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:SqP1997b,
author = {David McG. Squire and Thierry Pun},
title = {Using human partitionings of an image set to learn a similarity-based
	distance measure},
institution = {Computer Vision Group, Computing Centre, University of Geneva},
year = {1997},
number = {97.06},
address = {rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland},
month = {November},
url = {http://vision.unige.ch/publications/postscript/97/VGTR97.06_SquirePun.ps.gz},
abstract = {In this paper our goal is to employ human judgments of image similarity
	to improve the organization of an image database for content-based
	retrieval. We first derive a statistic, $\kappa_B$ for measuring
	the agreement between two partitionings of an image set into unlabeled
	subsets. This measure can be used both to measure the degree of agreement
	between pairs of human subjects, and also between human and machine
	partitionings of an image set. This provides a rigorous means of
	selecting between competing image database organization systems,
	and assessing how close the performance of such systems is to that
	which might be expected from a database organization done by hand.
	We then use the results of experiments in which human subjects are
	asked to partition a set of images into unlabeled subsets to define
	a similarity measure for pairs of images based on the frequency with
	which they were judged to be similar. We show that, when this measure
	is used to partition an image set using a clustering technique, the
	resultant clustering agrees better with those produced by human subjects
	than any of the feature space-based techniques investigated. Finally,
	we investigate the use of machine learning techniques to discover
	a mapping from a numerical feature space to this perceptual similarity
	space. Such a mapping would allow the ground truth knowledge abstracted
	from the human judgments to be generalized to unseen images.},
url1 = {http://vision.unige.ch/publications/postscript/97/VGTR97.06_SquirePun.pdf},
vgclass = {report},
vgproject = {viper}
}
@techreport{VG:Gar1995,
author = {Catherine De Garrini},
title = {Exploratory statistics for automated structuration of large image
	databases},
institution = {AI and Vision Group, Computing Centre, University of Geneva},
year = {1995},
number = {95.01},
address = {rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland},
month = {September},
vgclass = {report},
vgproject = {cbir}
}
@techreport{VG:Gar1994,
author = {Catherine De Garrini},
title = {D\'etermination de la translation, de la rotation et du facteur d'echelle
	d'un objet dans differents contextes},
institution = {AI and Vision Group, Computing Centre, University of Geneva},
year = {1994},
number = {94.08},
address = {rue G\'en\'eral Dufour, 24, CH-1211 Gen\`eve, Switzerland},
month = {October},
url = {ftp://cui.unige.ch/PUBLIC/vision/papers/degarrin/94.10.report.ps.Z},
vgclass = {report},
vgproject = {unspecified}
}
bib/viper_report.txt · Last modified: 2019/10/24 15:53 by marchand
--

Keywords: machine learning, information geometry, data mining, Big Data, affective information retrieval (recherche d'information), information visualisation, content-based image and video retrieval (CBIR, CBR, CBVR, CBMR, CBMIR), information mining, classification, multimedia and multimodal information management, semantic web, knowledge base (RDF, OWL, XML, metadata, auto-annotation, description), multimodal information fusion