Advertisement
Published in:
2011 | OriginalPaper | Chapter
Visual Word Aggregation
Authors : R. J. López-Sastre, J. Renes-Olalla, P. Gil-Jiménez, S. Maldonado-Bascón
Published in: Pattern Recognition and Image Analysis
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
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.