A Study On Combining Image Representations For Image Classification And Retrieval

A flexible description of images is offered by a cloud of points in a feature space. In the context of image retrieval such clouds can be represented in a number of ways. Two approaches are here considered. The first approach is based on the assumption of a normal distribution, hence homogeneous clouds, while the second one focuses on the boundary description, which is more suitable for multimodal clouds. The images are then compared either by using the Mahalanobis distance or by the support vector data description (SVDD), respectively. The paper investigates some possibilities of combining the image clouds based on the idea that responses of several cloud descriptions may convey a pattern, specific for semantically similar images. A ranking of image dissimilarities is used as a comparison for two image databases targeting image classification and retrieval problems. We show that combining of the SVDD descriptions improves the retrieval performance with respect to ranking, on the contrary to the Mahalanobis case. Surprisingly, it turns out that the ranking of the Mahalanobis distances works well also for inhomogeneous images.

[1]  Oded Maron,et al.  Multiple-Instance Learning for Natural Scene Classification , 1998, ICML.

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

[3]  Robert P. W. Duin,et al.  Support vector domain description , 1999, Pattern Recognit. Lett..

[4]  Robert P. W. Duin,et al.  Dissimilarity representations allow for building good classifiers , 2002, Pattern Recognit. Lett..

[5]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[6]  Kieron Messer.  Automatic image database retrieval system using adaptive colour and texture descriptors , 1999 .

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

[8]  Hiroshi Hasegawa,et al.  Multimedia information processing , 2004 .

[9]  Robert P. W. Duin,et al.  On Combining One-Class Classifiers for Image Database Retrieval , 2002, Multiple Classifier Systems.

[10]  James C. Bezdek,et al.  Decision templates for multiple classifier fusion: an experimental comparison , 2001, Pattern Recognit..

[11]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Thomas S. Huang,et al.  Content-based image retrieval with relevance feedback in MARS , 1997, Proceedings of International Conference on Image Processing.

[13]  Bernhard Schölkopf,et al.  Shrinking the Tube: A New Support Vector Regression Algorithm , 1998, NIPS.

[14]  B. Reljin,et al.  Adaptive Content-Based Image Retrieval with Relevance Feedback , 2005, EUROCON 2005 - The International Conference on "Computer as a Tool".

[15]  Ramesh C. Jain,et al.  Pattern Recognition Methods in Image and Video Databases: Past, Present and Future , 1998, SSPR/SPR.

[16]  Arnold W. M. Smeulders,et al.  Classification of images on the Internet by visual and textual information , 1999, Electronic Imaging.

[17]  David M. J. Tax,et al.  One-class classification , 2001 .

[18]  Josef Kittler,et al.  A region-based image database system using colour and texture , 1999, Pattern Recognit. Lett..