Application of Relevance Feedback in Content Based Image Retrieval Using Gaussian Mixture Models

In this paper a relevance feedback (RF) approach for content based image retrieval (CBIR) is described and evaluated. The approach uses Gaussian mixture (GM) models of the image features and a query that is updated in a probabilistic manner. This update reflects the preferences of the user and is based on the models of both positive and negative feedback images. Retrieval is based on a recently proposed distance measure between probability density functions (pdfs), which can be computed in closed form for GM models. The proposed approach takes advantage of the form of this distance measure and updates it very efficiently based on the models of the user specified relevant and irrelevant images. For evaluation purposes, comparative experimental results are presented that demonstrate the merits of the proposed methodology.

[1]  Ling Guan,et al.  Automatic machine interactions for content-based image retrieval using a self-organizing tree map architecture , 2002, IEEE Trans. Neural Networks.

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

[3]  Thomas S. Huang,et al.  Optimizing learning in image retrieval , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[4]  Hayit Greenspan,et al.  Context-dependent segmentation and matching in image databases , 2004, Comput. Vis. Image Underst..

[5]  Nuno Vasconcelos,et al.  On the efficient evaluation of probabilistic similarity functions for image retrieval , 2004, IEEE Transactions on Information Theory.

[6]  Nikolas P. Galatsanos,et al.  A Relevance Feedback Approach for Content Based Image Retrieval Using Gaussian Mixture Models , 2006, ICANN.

[7]  Nikolas P. Galatsanos,et al.  A similarity learning approach to content-based image retrieval: application to digital mammography , 2004, IEEE Transactions on Medical Imaging.

[8]  Yixin Chen,et al.  A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Ingemar J. Cox,et al.  The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments , 2000, IEEE Trans. Image Process..

[10]  Sugata Ghosal,et al.  An image retrieval system with automatic query modification , 2002, IEEE Trans. Multim..

[11]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[12]  James Ze Wang,et al.  Content-based image retrieval: approaches and trends of the new age , 2005, MIR '05.

[13]  Bo Zhang,et al.  Relevance feedback in region-based image retrieval , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[14]  Djemel Ziou,et al.  Combining positive and negative examples in relevance feedback for content-based image retrieval , 2003, J. Vis. Commun. Image Represent..

[15]  Djemel Ziou,et al.  Relevance feedback for CBIR: a new approach based on probabilistic feature weighting with positive and negative examples , 2006, IEEE Transactions on Image Processing.

[16]  Nikos A. Vlassis,et al.  A Greedy EM Algorithm for Gaussian Mixture Learning , 2002, Neural Processing Letters.

[17]  Nikolas P. Galatsanos,et al.  An Analytic Distance Metric for Gaussian Mixture Models with Application in Image Retrieval , 2005, ICANN.

[18]  Jitendra Malik,et al.  Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Nuno Vasconcelos,et al.  Learning from User Feedback in Image Retrieval Systems , 1999, NIPS.

[20]  Fabio Roli,et al.  Bayesian relevance feedback for content-based image retrieval , 2004, Pattern Recognit..

[21]  Wei-Ying Ma,et al.  Learning a semantic space from user's relevance feedback for image retrieval , 2003, IEEE Trans. Circuits Syst. Video Technol..

[22]  Bo Zhang,et al.  Gaussian mixture model for relevance feedback in image retrieval , 2002, Proceedings. IEEE International Conference on Multimedia and Expo.

[23]  Shaoping Ma,et al.  Relevance feedback in content-based image retrieval: Bayesian framework, feature subspaces, and progressive learning , 2003, IEEE Trans. Image Process..

[24]  Thomas S. Huang,et al.  Relevance feedback: a power tool for interactive content-based image retrieval , 1998, IEEE Trans. Circuits Syst. Video Technol..

[25]  Wei-Ying Ma,et al.  Learning similarity measure for natural image retrieval with relevance feedback , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[26]  Hong-Jiang Zhang,et al.  An efficient and effective region-based image retrieval framework , 2004, IEEE Transactions on Image Processing.

[27]  Thomas S. Huang,et al.  Image processing , 1971 .

[28]  Nuno Vasconcelos,et al.  Learning Over Multiple Temporal Scales in Image Databases , 2000, ECCV.

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

[30]  B. S. Manjunath,et al.  Color and texture descriptors , 2001, IEEE Trans. Circuits Syst. Video Technol..

[31]  Edward Y. Chang,et al.  Support vector machine active learning for image retrieval , 2001, MULTIMEDIA '01.

[32]  Inderjit S. Dhillon,et al.  Differential Entropic Clustering of Multivariate Gaussians , 2006, NIPS.

[33]  Shih-Fu Chang,et al.  Image Retrieval: Current Techniques, Promising Directions, and Open Issues , 1999, J. Vis. Commun. Image Represent..

[34]  C.-Y. Li,et al.  Relevance feedback using generalized Bayesian framework with region-based optimization learning , 2005, IEEE Transactions on Image Processing.

[35]  Christos Faloutsos,et al.  MindReader: Querying Databases Through Multiple Examples , 1998, VLDB.

[36]  P. Deb.  Finite Mixture Models , 2008 .