@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},
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
shortly addressed, such as obtaining relevance judgments and choosing
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
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},
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
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
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},
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},
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
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.
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}
}