Agglomerative clustering using hybrid features for image categorization

Karina Damico; Roxanne L. Canosa

doi:10.1117/12.2042358

3 March 2014 Agglomerative clustering using hybrid features for image categorization

Karina Damico, Roxanne L. Canosa

Proceedings Volume 9027, Imaging and Multimedia Analytics in a Web and Mobile World 2014; 90270N (2014) https://doi.org/10.1117/12.2042358
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States

Abstract

This research project describes an agglomerative image clustering technique that is used for the purpose of automating image categorization. The system is implemented in two stages: feature vector formation, and feature space clustering. The features that we selected are based on texture salience (Gabor filters and a binary pattern descriptor). Global properties are encoded via a hierarchical spatial pyramid and local structure is encoded as a bit string, retained via a set of histograms. The transform can be computed efficiently – it involves only 16 operations (8 comparisons and 8 additions) per 3x3 region. A disadvantage is that it is not invariant to rotation or scale changes; however, the spatial pyramid representing global structure helps to ameliorate this problem. An agglomerative clustering technique is implemented and evaluated based on ground-truth values and a human subjective rating.

Citation Download Citation

Karina Damico and Roxanne L. Canosa "Agglomerative clustering using hybrid features for image categorization", Proc. SPIE 9027, Imaging and Multimedia Analytics in a Web and Mobile World 2014, 90270N (3 March 2014); https://doi.org/10.1117/12.2042358

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