@Article{roman:EAAI2019,
author    = {Edgar Roman-Rangel and Stephane Marchand-Maillet},
title     = {Inductive t-SNE via deep learning to visualize multi-label images},
journal   = {Eng. Appl. of {AI}},
volume    = {81},
pages     = {336--345},
year      = {2019},
url       = {https://doi.org/10.1016/j.engappai.2019.01.015}

}

@Article{krulis:MTAP2017,
author    = {Martin Krulis and Hasmik Osipyan and Stephane Marchand-Maillet},
title     = {Employing {GPU} architectures for permutation-based indexing},
journal   = {Multimedia Tools and Applications},
volume    = {76},
issue = {9},
year      = {2017},
url       = {https://link.springer.com/article/10.1007/s11042-016-3677-7},
}
@Article{nielsen:entropy2017,
author    = {Frank Nielsen and Ke Sun and Stephane Marchand-Maillet},
title     = {On Holder Projective Divergences},
journal   = {Entropy},
volume    = {19},
issue = {3},
year      = {2017},
url       = {http://www.mdpi.com/1099-4300/19/3/122/pdf},
}
@Article{romanrangel:jocch2016,
author    = {Edgar Roman-Rangel and Diego Jimenez-Badillo and Stephane Marchand-Maillet},
title     = {Classification and Retrieval of Archaeological Potsherds using Histograms of Spherical Orientations},
journal   = {Journal on Computing and Cultural Heritage},
volume    = {9},
issue = {3},
year      = {2016},
url       = {http://dl.acm.org/citation.cfm?id=2948069},

}

@Article{romanrangel:neurocomputing2016,
author    = {Edgar Roman-Rangel and Changhu Wang and Stephane Marchand-Maillet},
title     = {SimMap: Similarity maps for scale invariant local shape descriptors},
journal   = {Neurocomputing},
volume    = {175},
pages     = {888--898},
year      = {2016},
url       = {http://dx.doi.org/10.1016/j.neucom.2015.06.093},

}

@Article{rui:ieeesigprocmag2015,
author    = {Rui Hu and Gulcan Can and Carlos Pallan Gayol and Guido Krempel and
             Jakub Spotak and Gabrielle Vail and Stephane Marchand-Maillet and Jean-Marc Odobez and Daniel Gatica-Perez},
title     = {Multimedia Analysis and Access of Ancient Maya Epigraphy: Tools to support scholars on Maya hieroglyphics},
journal   = {{IEEE} Signal Processing Magazine},
volume    = {32},
number    = {4},
pages     = {75--84},
year      = {2015},
url       = {http://dx.doi.org/10.1109/MSP.2015.2411291}, 

}

@Article{romanrangel:PR2015,
author    = {Edgar Roman-Rangel and Stephane Marchand-Maillet},
title     = {Shape-based detection of Maya hieroglyphs using weighted bag representations},
journal   = {Pattern Recognition},
volume    = {48},
number    = {4},
pages     = {1161--1173},
year      = {2015},
url       = {http://dx.doi.org/10.1016/j.patcog.2014.06.009},

}

 @Article{mohamed:informationsystems2015,
  author = 	 {Hisham Mohamed and Stéphane Marchand-Maillet},
  title = 	 {Quantized Ranking for Permutation-Based Indexing},
  journal = 	 {Information Systems},
  number    = {52},
  pages     = {163-175},
  year = 	 {2015},
  url = {http://dx.doi.org/10.1016/j.is.2015.01.009}

}

 @Article{weng:tifs2015,
  author = 	 {Li Weng and Laurent Amsaleg and April Morton and Stephane Marchand-Maillet},
  title = 	 {A Privacy-Preserving Framework for Large-Scale Content-Based Information Retrieval},
  journal = 	 {IEEE Transactions on Information Forensics and Security},
  year = 	 {2015},
  volume = {10},
  number = {1},
  url = {http://dx.doi.org/10.1109/TIFS.2014.2365998}

}

 @Article{mohamed:parallelcomputing2013,
  author = 	 {Hisham Mohamed and Stéphane Marchand-Maillet},
  title = 	 {MRO-MPI: MapReduce overlapping using MPI and an optimized data exchange policy},
  journal = 	 {Parallel Computing},
  year = 	 {2013},
  volume = {39},
  number = {12}

}

 @Article{marchand:emisa2014,
  author = 	 {Stéphane Marchand-Maillet and Birgit Hofreiter},
  title = 	 {Big Data Management and Analysis for Business Informatics},
  journal = 	 {Enterprise Modelling and Information Systems Architectures (EMISA)},
  year = 	 {2014},
  volume = {9},
  url = {http://www.wi-inf.uni-duisburg-essen.de/FGFrank/documents/Sonstige/EMISA-9%281%29-Roadmap-Business-Informatics.pdf}

}

 @Article{marchand:mtap2014,
  author = 	 {Stéphane Marchand-Maillet and Patrick Lambert and Bernard Merialdo and Jeny Benois},
  title = 	 {Introduction to the special issue on content-based image retrieval},
  journal = 	 {Multimedia Tools and Applications},
  year = 	 {2014},
  number= {2},
  volume= {69},
  month = {February}

}

@article{mohamed:mtap2014,
          year={2014},
          journal={Multimedia Tools and Applications},
          title={Distributed media indexing based on MPI and MapReduce},
          volume={69},
          number ={2},
          publisher={Springer US},
          author={Mohamed, Hisham and Marchand-Maillet, Stéphane},
          url={http://dx.doi.org/10.1007/s11042-012-1283-x}

}

 @Article{morrison:mtap2013,
  author = 	 {Morrison, D. and Tsikrika, T. and Hollink, V. and de Vries, A. P. and Bruno, E. and Marchand-Maillet, S.},
  title = 	 {Topic Modelling of clickthrough data in image search},
  journal = 	 {Multimedia Tools and Applications},
  year = 	 {2013},
  volume = 	 {66},
  number = 	 {3}
}
 @Article{kompatsiaris:mtap2011,
  author = 	 {Ioannis Kompatsiaris and Stéphane Marchand-Maillet and Roelof van Zwol and Sébastien Marcel},
  title = 	 {Introduction to the special issue on image and video retrieval: theory and applications},
  journal = 	 {Multimedia Tools and Applications},
  year = 	 {2011},
  volume = {55},
  number = 	 {1},
  url       = {http://dx.doi.org/10.1007/s11042-010-0618-8}
}
@article{bruno2009:jmm,
author = {Eric Bruno and Stephane Marchand-Maillet},
title = {Multimodal Preference Aggregation for Multimedia Information Retrieval},
journal = {Journal of Multimedia},
year = 2009,
      volume = 4,
      number = 5,
      pages = 321-329,
url = {http://www.academypublisher.com/jmm/vol04/no05/jmm0405321329.pdf}
}
@article{kludas2008:mtap,
author = {Jana Kludas and Eric Bruno and Stephane Marchand-Maillet},
title = {Can Feature Information Interaction help for Information Fusion in Multimedia Problems?},
journal = {Multimedia Tools and Applications Journal special issue on "Metadata Mining for Image Understanding"},
      note = {DOI: http://dx.doi.org/10.1007/s11042-008-0251-y},
      volume = {42},
      number={1},
      pages={57-71},
year = 2008,
url = {http://viper.unige.ch/documents/pdf/kludas2008-mtap.pdf}
}
@article{kosinov2008:paa,
author = {S. Kosinov and T Pun},
title = {Distance-based discriminant analysis method and its applications},
journal = {Pattern Analysis and Applications},
year = {2008},
      volume = {11},
      number={3-4},
      pages = {227-246},
      note = {(DOI: 10.1007/s10044-007-0082-x)},
url = {http://viper.unige.ch/documents/pdf/kosinov2008-paa.pdf}
}
@article{kosinov2008:spic,
author = {S. Kosinov and Eric Bruno and  St\'ephane Marchand-Maillet},
title = {Spatially-consistent partial matching for intra- and inter-image prototype selection},
journal = {Signal Processing: Image Communication special issue on "Semantic Analysis for Interactive Multimedia Services"},
year = {2008},
      volume = {23},
      pages = {516-524},
      note = {(DOI: 10.1016/j.image.2008.04.017)},
url = {http://viper.unige.ch/documents/pdf/kosinov2008-spic.pdf}
}
@article{Bruno08,
author = {Eric Bruno and Nicolas Mo\"enne-Loccoz and St\'ephane Marchand-Maillet},
title = {Design of multimodal dissimilarity spaces for retrieval of multimedia documents},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
year = {2008},
      number = {9},
      volume = {30},
      pages = {1520-1533},
url = {http://viper.unige.ch/documents/pdf/bruno2008-pami.pdf},
      note = {(http://doi.ieeecomputersociety.org/10.1109/TPAMI.2007.70811)}
}
@article{Moenne-Loccoz2006,
author = {Nicolas Mo\"enne-Loccoz and Bruno Janvier and St\'ephane Marchand-Maillet
	and Eric Bruno},
title = {Handling Temporal Heterogeneous Data for Content-Based Management
	of Large Video Collections},
journal = {Multimedia Tools and Applications},
year = {2006},
volume = {31},
pages = {309-325},
url = {http://www.springerlink.com/content/b323103150v76742/},
owner = {beekhof},
timestamp = {2007.10.09}
}
@article{Janvier2006a,
author = {Bruno Janvier and Eric Bruno and St\'ephane Marchand-Maillet and
	Thierry Pun},
title = {Information-theoretic temporal segmentation of videos and applications:
	multiscale keyframe selection and transition detection},
journal = {Multimedia Tools and Applications},
year = {2006},
volume = {30},
pages = {273-288},
url = {http://viper.unige.ch/documents/pdf/janvier2006-mtap.pdf}
}
@article{moennePAA2004,
author = {Nicolas Mo\"enne-Loccoz and Eric Bruno and St{\'e}phane Marchand-Maillet},
title = {Knowledge-based Detection of Events in Video Streams from Salient Regions of Activities},
journal = {Pattern Analysis and Applications (PAA), special issue Video Event Mining},
year = 2004,	
      volume = {7},
number = 4,
pages = {422-429},
month = {December},
note = {DOI: 10.1007/s10044-004-0235-0},
url = {http://viper.unige.ch/documents/pdf/moenneloccoz2004-paa.pdf}
}
@article{Bruno2004,
author = {Eric Bruno and Nicolas Mo\"enne-Loccoz and St\'ephane Marchand-Maillet},
title = {Unsupervised Event Discrimination Based on Nonlinear Temporal Modelling of Activity},
journal = {Pattern Analysis and Application},
year = 2004,
volume = {7},
number = 4,
pages = {402-410},
month = {December},
url = {http://viper.unige.ch/documents/pdf/bruno2004-paa.pdf}
}
@article{VG:IJCV03,
author = {Henning M{\"u}ller and David McG. Squire and Thierry Pun},
title = {Learning from user behavior in image retrieval: application of the
	market basket analysis},
journal = {Int. J. of Comp. Vision, Special Issue on Content-Based Image Retrieval,
	to appear},
year = {2003},
vgclass = {refpap},
vgproject = {viper}
}

@article{bruno_ieeeip03,

author = {Matthias Pingault and Eric Bruno and Denis Pellerin},
title = {A Robust Multiscale B-spline Function Decomposition Process to Motion Transparency Estimation},
journal = {IEEE Transactions on Image Processing },
month = {November},
year = {2003},
address = {},
pages = {1416- 1426},
number = {11},
volume = {12},
url = {http://ieeexplore.ieee.org/xpl/abs_free.jsp?isNumber=27803&prod=JNL&arnumber=1240108&arSt=+1416&ared=+1426&arAuthor=+Pingault,+M.;++Bruno,+E.;++Pellerin,+D.&arNumber=1240108&a_id0=1240098&a_id1=1240099&a_id2=1240100&a_id3=1240101&a_id4=1240102&a_id5=1240103&a_id6=1240104&a_id7=1240105&a_id8=1240106&a_id9=1240107&a_id10=1240108&a_id11=1240109&count=12}

}

@article{bruno_sp02,

author = {Eric Bruno and Denis Pellerin},
title = {Robust Motion Estimation using Spatial Gabor-like filters},
journal = {Signal Processing},
month = {February},
year = {2002},
address = {},
pages = {297-309},
number = {2},
volume = {82},
url = {http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V18-44P6W8N-2&_coverDate=02/28/2002&_alid=134563986&_rdoc=1&_fmt=&_orig=search&_qd=1&_cdi=5668&_sort=d&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=46eb9d7d8e4cc40c904a33514250c1f0}

}

@article{VG:Mue2002,
author = {Henning M{\"u}ller},
title = {{J}\"ager des verlorenen {F}otos - {D}as {GNU} {I}mage {F}inding
	{T}ool in der {P}raxis},
journal = {ct Magazin {f\"ur} Computertechnik},
year = {2002},
volume = {6},
pages = {252--257},
vgclass = {otherjournals},
vgproject = {viper}
}
@article{VG:MMM2002,
author = {Henning M{\"u}ller and Wolfgang M{\"u}ller and St{\'e}phane Marchand-Maillet
	and David McG. Squire and Thierry Pun},
title = {A Framework for Benchmarking in Visual Information Retrieval},
journal = {International Journal on Multimedia Tools and Applications},
year = {2002},
note = {(Special Issue on Multimedia Information Retrieval) - to appear},
vgclass = {refpap},
vgproject = {viper}
}
@article{VG:Mue2001,
author = {Henning M{\"u}ller},
title = {{S}uchen ohne {W}orte -- wie inhaltsbasierte {S}uche funktioniert},
journal = {ct Magazin {f\"ur} Computertechnik},
year = {2001},
volume = {15},
pages = {162--172},
vgclass = {otherjournals},
vgproject = {viper}
}
@article{VG:MMS2001,
author = {Henning M{\"u}ller and Wolfgang M{\"u}ller and David McG. Squire
	and St{\'e}phane Marchand-Maillet and Thierry Pun},
title = {Performance Evaluation in Content-Based Image Retrieval: Overview
	and Proposals},
journal = {Pattern Recognition Letters (Special Issue on Image and Video Indexing)},
year = {2001},
volume = {22},
number = {5},
pages = {593--601},
note = {H. Bunke and X. Jiang Eds.},
url = {http://www.elsevier.nl/gej-ng/10/35/61/49/29/36/abstract.html},
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 defining 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. (c) Copyright 2001, Elsevier
	Science, All rights reserved.},
vgclass = {refpap},
vgproject = {viper}
}
@article{VG:MMMSP2001a,
author = {Wolfgang M{\"u}ller and St{\'e}phane Marchand-Maillet and Henning
	M{\"u}ller and David McG. Squire and Thierry Pun},
title = {Evaluating Image Browsers Using Structured Annotation},
journal = {Journal of American for Information Science and Technology (JASIST)},
year = {2001},
volume = {52},
number = {11},
vgclass = {refpap},
vgproject = {viper}
}
@article{VG:Squ2000,
author = {David McG. Squire},
title = {Distance Learning Networks: learning a similarity-based distance
	measure for content--based image retrieval},
journal = {Journal of Visual Communication and Image Representation},
year = {2000},
abstract = {In this paper we employ human judgments of image similarity to learn
	a distance measure for content--based image retrieval. We first derive
	a statistic, $\kappa_B$, for measuring the agreement between two
	partitionings of an image set into unlabeled subsets. We then use
	the results of experiments in which human subjects partition a set
	of images into unlabeled subsets to define a similarity measure for
	pairs of images based on the frequency with which they are 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 show that
	a learning technique based on an extension of a Kohonen network allows
	a mapping from a numerical feature space to this perceptual similarity
	space to be learnt which results in partitionings in excellent agreement
	with those produced by human subjects.},
vgclass = {refpap},
vgproject = {viper}
}
@article{VG:SqC2000,
author = {David McG. Squire and Terry M. Caelli},
title = {Invariance Signatures: Characterizing contours by their departures
	from invariance},
journal = {Computer Vision and Image Understanding},
year = {2000},
volume = {77},
number = {3},
pages = {284--316},
month = {mar},
url = {http://vision.unige.ch/publications/postscript/99/SquireCaelli_cviu99.ps.gz},
abstract = {In this paper, a new invariant feature of two-dimensional contours
	is reported: the Invariance Signature. The Invariance Signature is
	a measure of the degree to which a contour is invariant under a variety
	of transformations, derived from the theory of Lie transformation
	groups. It is shown that the Invariance Signature is itself invariant
	under shift, rotation and scaling of the contour. Since it is derived
	from local properties of the contour, it is well-suited to a neural
	network implementation. It is shown that a Model-Based Neural Network
	(MBNN) can be constructed which computes the Invariance Signature
	of a contour, and classifies patterns on this basis. Experiments
	demonstrate that Invariance Signature networks can be employed successfully
	for shift-, rotation- and scale-invariant optical character recognition.},
url1 = {http://vision.unige.ch/publications/postscript/99/SquireCaelli_cviu99.pdf},
vgclass = {refpap},
vgproject = {viper}
}
@article{VG:SMM2000a,
author = {David McG. Squire and Wolfgang M{\"u}ller and Henning M{\"u}ller
	and Thierry Pun},
title = {Content-based query of image databases: inspirations from text retrieval},
journal = {Pattern Recognition Letters (Selected Papers from The 11th Scandinavian
	Conference on Image Analysis SCIA '99)},
year = {2000},
volume = {21},
number = {13-14},
pages = {1193-1198},
note = {B.K. Ersboll, P. Johansen, Eds.},
url = {http://www.elsevier.nl/gej-ng/10/35/61/45/36/34/article.pdf},
abstract = {This paper reports the application of techniques inspired by text
	retrieval research to content-based image retrieval. In particular,
	we show how the use of an inverted file data structure permits the
	use of an extremely high-dimensional feature-space, 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 weighting scheme based on feature frequencies is used to combine
	disparate 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.},
vgclass = {refpap},
vgproject = {viper}
}
@article{VG:SqP1998,
author = {David McG. Squire and Thierry Pun},
title = {Assessing Agreement Between Human and Machine Clusterings of Image
	Databases},
journal = {Pattern Recognition},
year = {1998},
volume = {31},
number = {12},
pages = {1905--1919},
url = {http://vision.unige.ch/publications/postscript/98/SquirePun_pr.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/98/SquirePun_pr.pdf},
vgclass = {refpap},
vgproject = {viper}
}
@article{VG:PuS1996,
author = {Thierry Pun and David McG. Squire},
title = {Statistical structuring of pictorial databases for content-based
	image retrieval systems},
journal = {Pattern Recognition Letters},
year = {1996},
volume = {17},
pages = {1299--1310},
keywords = {image databases, content-based image retrieval systems, exploratory
	statistics, correspondence analysis, ascendant hierarchical classification},
url = {http://vision.unige.ch/publications/postscript/96/PuS96_prl_corran.ps.gz},
abstract = {This letter presents a two-stage statistical approach for ``exploring
	and explaining'' a pictorial database, for content-based image retrieval
	systems. First, we describe how correspondence analysis provides
	images classes, as well as facilitates the understanding of the role
	of image primatives and attributes used to index pictures. Such understanding
	allows an intelligent choice of features, and thus computational
	savings, to be made. Second, ascendant heirarchical classification
	permits the structuring of the database, in order to ease picture
	indexing and retrieval.},
url1 = {http://vision.unige.ch/publications/postscript/96/PuS96_prl_corran.pdf},
vgclass = {refpap},
vgproject = {viper}
}
bib/viper_journal.txt · Last modified: 2019/10/24 15:47 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