Visual word aggregation

Authors:
R. J. López-Sastre;J. Renes-Olalla;P. Gil-Jiménez;S. Maldonado-Bascón
Affiliations:
GRAM, Department of Signal Theory and Communications, University of Alcalá;GRAM, Department of Signal Theory and Communications, University of Alcalá;GRAM, Department of Signal Theory and Communications, University of Alcalá;GRAM, Department of Signal Theory and Communications, University of Alcalá
Venue:
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Year:
2011

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Abstract

Most recent category-level object recognition systems work with visual words, i.e. vector quantized local descriptors. These visual vocabularies are usually constructed by using a single method such as K-means for clustering the descriptor vectors of patches sampled either densely or sparsely from a set of training images. Instead, in this paper we propose a novel methodology for building efficient codebooks for visual recognition using clustering aggregation techniques: the Visual Word Aggregation (VWA). Our aim is threefold: to increase the stability of the visual vocabulary construction process; to increase the image classification rate; and also to automatically determine the size of the visual codebook. Results on image classification are presented on the testbed PASCAL VOC Challenge 2007.