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Proceedings Paper

Agglomerative clustering using hybrid features for image categorization
Author(s): Karina Damico; Roxanne L. Canosa
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Paper 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.

Paper Details

Date Published: 3 March 2014
PDF: 7 pages
Proc. SPIE 9027, Imaging and Multimedia Analytics in a Web and Mobile World 2014, 90270N (3 March 2014); doi: 10.1117/12.2042358
Show Author Affiliations
Karina Damico, Rochester Institute of Technology (United States)
Roxanne L. Canosa, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 9027:
Imaging and Multimedia Analytics in a Web and Mobile World 2014
Qian Lin; Jan Philip Allebach; Zhigang Fan, Editor(s)

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