Adaptive Multi-class Metric Content-Based Image Retrieval

Relevance feedback methods for content-based image retrieval have shown promise in a variety of image database applications. These techniques assume two (relevant and irrelevant) class relevance feedback. While simple computationally, two class relevance feedback often becomes inadequate in providing sufficient information to help rapidly improve retrieval performance. We propose a locally adaptive technique for content-based image retrieval that enables relevance feedback to take on multi-class form. We estimate a flexible multi-class metric for computing retrievals based on Chi-squared distance analysis. As a result, local data distributions can be sufficiently exploited, whereby rapid performance improvement can be achieved. The efficacy of our method is validated and compared against other competing techniques using a number of real world data sets.

[1]  Robert Tibshirani,et al.  Discriminant Adaptive Nearest Neighbor Classification , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  David J. Hand,et al.  The multi-class metric problem in nearest neighbour discrimination rules , 1990, Pattern Recognit..

[3]  Nuno Vasconcelos,et al.  A probabilistic architecture for content-based image retrieval , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[4]  Bir Bhanu,et al.  Feature Relevance Estimation for Image Databases , 1999, Multimedia Information Systems.

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

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

[7]  David L. Waltz,et al.  Trading MIPS and memory for knowledge engineering , 1992, CACM.

[8]  Bir Bhanu,et al.  Probabilistic Feature Relevance Learning for Content-Based Image Retrieval , 1999, Comput. Vis. Image Underst..

[9]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[10]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.