Combining similarity measures in content-based image retrieval

The purpose of content based image retrieval (CBIR) systems is to allow users to retrieve pictures from large image repositories. In a CBIR system, an image is usually represented as a set of low level descriptors from which a series of underlying similarity or distance functions are used to conveniently drive the different types of queries. Recent work deals with combination of distances or scores from different and usually independent representations in an attempt to induce high level semantics from the low level descriptors of the images. Choosing the best method to combine these results requires a careful analysis and, in most cases, the use of ad-hoc strategies. Combination based on or derived from product and sum rules are common approaches. In this paper we propose a method to combine a given set of dissimilarity functions. For each similarity function, a probability distribution is built. Assuming statistical independence, these are used to design a new similarity measure which combines the results obtained with each independent function.

[1]  Joachim M. Buhmann,et al.  Empirical Evaluation of Dissimilarity Measures for Color and Texture , 2001, Comput. Vis. Image Underst..

[2]  Ian Burns,et al.  Measuring texture classification algorithms , 1997, Pattern Recognit. Lett..

[3]  Ebroul Izquierdo,et al.  Optimizing Metrics Combining Low-Level Visual Descriptors for Image Annotation and Retrieval , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[4]  Pablo Castells,et al.  Probabilistic Score Normalization for Rank Aggregation , 2006, ECIR.

[5]  Stéphane Marchand-Maillet,et al.  Combining multimodal preferences for multimedia information retrieval , 2007, MIR '07.

[6]  David G. Stork,et al.  Pattern Classification , 1973 .

[7]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[8]  Wan-Chi Siu,et al.  Multimedia Information Retrieval and Management: Technological Fundamentals and Applications , 2010 .

[9]  Mohan M. Trivedi,et al.  Segmentation of a high-resolution urban scene using texture operators , 1984, Comput. Vis. Graph. Image Process..

[10]  Ramin Zabih,et al.  Histogram refinement for content-based image retrieval , 1996, Proceedings Third IEEE Workshop on Applications of Computer Vision. WACV'96.

[11]  Dirk Neumann,et al.  Image retrieval and perceptual similarity , 2006, TAP.

[12]  Edward A. Fox,et al.  A new framework to combine descriptors for content-based image retrieval , 2005, CIKM '05.

[13]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[14]  Joni-Kristian Kämäräinen,et al.  Improving similarity measures of histograms using smoothing projections , 2003, Pattern Recognit. Lett..

[15]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Guillermo Ayala,et al.  Spatial Size Distributions: Applications to Shape and Texture Analysis , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Tat-Seng Chua,et al.  Integrated graph-based semi-supervised multiple/single instance learning framework for image annotation , 2008, ACM Multimedia.

[18]  Giorgio Giacinto,et al.  Nearest-prototype relevance feedback for content based image retrieval , 2004, ICPR 2004.

[19]  Guillermo Ayala,et al.  A novel Bayesian framework for relevance feedback in image content-based retrieval systems , 2006, Pattern Recognit..

[20]  Minh N. Do,et al.  Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance , 2002, IEEE Trans. Image Process..

[21]  B. Silverman.  Density estimation for statistics and data analysis , 1986 .

[22]  Jake K. Aggarwal,et al.  Combining structure, color and texture for image retrieval: A performance evaluation , 2002, Object recognition supported by user interaction for service robots.

[23]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[24]  James Ze Wang,et al.  Image retrieval: Ideas, influences, and trends of the new age , 2008, CSUR.

[25]  Esther de Ves,et al.  Applying logistic regression to relevance feedback in image retrieval systems , 2007, Pattern Recognit..

[26]  Yong Wang,et al.  Combining global, regional and contextual features for automatic image annotation , 2009, Pattern Recognit..

[27]  Alireza Khotanzad,et al.  Invariant Image Recognition by Zernike Moments , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  E. Dougherty,et al.  Gray-scale morphological granulometric texture classification , 1994 .

[29]  Joemon M. Jose,et al.  Evidence combination for multi-point query learning in content-based image retrieval , 2004, IEEE Sixth International Symposium on Multimedia Software Engineering.

[30]  Esther de Ves,et al.  Selecting the structuring element for morphological texture classification , 2006, Pattern Analysis and Applications.

[31]  Norbert Fuhr,et al.  From Retrieval Status Values to Probabilities of Relevance for Advanced IR Applications , 2004, Information Retrieval.

[32]  C.-C. Jay Kuo,et al.  Wavelet descriptor of planar curves: theory and applications , 1996, IEEE Trans. Image Process..

[33]  Claudio Gutierrez,et al.  Survey of graph database models , 2008, CSUR.

[34]  Edward Y. Chang,et al.  Discovery of a perceptual distance function for measuring image similarity , 2003, Multimedia Systems.

[35]  M. C. Jones,et al.  A reliable data-based bandwidth selection method for kernel density estimation , 1991 .

[36]  R. Manmatha,et al.  Modeling score distributions for combining the outputs of search engines , 2001, SIGIR '01.

[37]  Nicu Sebe,et al.  Content-based multimedia information retrieval: State of the art and challenges , 2006, TOMCCAP.

[38]  Juan Domingo,et al.  Probabilistic normalization: an approach to normalizing similarity measures in content based image retrieval , 2008 .

[39]  Rama Chellappa,et al.  Classification of textures using Gaussian Markov random fields , 1985, IEEE Trans. Acoust. Speech Signal Process..

[40]  Arun Ross,et al.  Score normalization in multimodal biometric systems , 2005, Pattern Recognit..

[41]  Anil K. Jain,et al.  Decision-level fusion in fingerprint verification , 2001, Pattern Recognit..

[42]  M. Vetterli,et al.  Wavelet-Based Texture Retrieval Using Generalized , 2002 .

[43]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[44]  Thierry Pun,et al.  Performance evaluation in content-based image retrieval: overview and proposals , 2001, Pattern Recognit. Lett..