Image recognition for digital libraries

The interpretation of natural scenes, generally so obvious and effortless for humans, still remains a challenge in computer vision. To allow the search of image-based documents in digital libraries, we propose to design classifiers able to annotate images with keywords. First, we propose an image representation appropriate for scene description. Images are segmented into regions, and then indexed according to the presence of given region types. Second, we propound a classification scheme designed to separate images in the descriptor space. This is achieved by combining feature selection and kernel-method-based classification

[1]  Pasquale Pagano,et al.  OpenDLib: A Digital Library Service System , 2002, ECDL.

[2]  Jitendra Malik,et al.  Recognition of Images in Large Databases Using a Learning Framework , 1997 .

[3]  Pavel Zezula,et al.  Tree Signatures for XML Querying and Navigation , 2003, Xsym.

[4]  Christian Böhm,et al.  Searching in high-dimensional spaces: Index structures for improving the performance of multimedia databases , 2001, CSUR.

[5]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[6]  James Ze Wang,et al.  Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Dorin Comaniciu,et al.  Robust analysis of feature spaces: color image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Pavel Zezula,et al.  YAPI: Yet Another Path Index for XML Searching , 2003, ECDL.

[9]  Michael S. Lew,et al.  Principles of Visual Information Retrieval , 2001, Advances in Pattern Recognition.

[10]  Roberto Battiti,et al.  Using mutual information for selecting features in supervised neural net learning , 1994, IEEE Trans. Neural Networks.

[11]  Michael S. Lew.  Next-Generation Web Searches for Visual Content , 2000, Computer.

[12]  B. S. Manjunath,et al.  Introduction to MPEG-7: Multimedia Content Description Interface , 2002 .

[13]  S. Boag,et al.  XQuery 1.0 : An XML query language, W3C Working Draft 12 November 2003 , 2003 .

[14]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[15]  Alberto Del Bimbo,et al.  Visual information retrieval , 1999 .

[16]  Nello Cristianini,et al.  The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines , 1998, ICML.

[17]  R. Gray.  Entropy and Information Theory , 1990, Springer New York.

[18]  David A. Forsyth,et al.  Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary , 2002, ECCV.

[19]  Tom Minka,et al.  Interactive learning with a "society of models" , 1997, Pattern Recognit..

[20]  Dustin Boswell,et al.  Introduction to Support Vector Machines , 2002 .

[21]  Vapnik,et al.  SVMs for Histogram Based Image Classification , 1999 .

[22]  Claudio Gennaro,et al.  Milos: A Multimedia Content Management System for Digital Library Applications , 2004, ECDL.

[23]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[24]  Patrick Haffner,et al.  Support vector machines for histogram-based image classification , 1999, IEEE Trans. Neural Networks.

[25]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[26]  Rosalind W. Picard,et al.  Interactive Learning Using a "Society of Models" , 2017, CVPR 1996.

[27]  Nozha Boujemaa,et al.  Unsupervised robust clustering for image database categorization , 2002, Object recognition supported by user interaction for service robots.

[28]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.