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. © 2011 Springer-Verlag.
CITATION STYLE
López-Sastre, R. J., Renes-Olalla, J., Gil-Jiménez, P., & Maldonado-Bascón, S. (2011). Visual word aggregation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6669 LNCS, pp. 676–683). https://doi.org/10.1007/978-3-642-21257-4_84
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