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A similarity measure between unordered vector sets with application to image categorization

8 years 10 months ago
A similarity measure between unordered vector sets with application to image categorization
We present a novel approach to compute the similarity between two unordered variable-sized vector sets. To solve this problem, several authors have proposed to model each vector set with a Gaussian mixture model (GMM) and to compute a probabilistic measure of similarity between the GMMs. The main contribution of this paper is to model each vector set with a GMM adapted from a common "universal" GMM using the maximum a posteriori (MAP) criterion. The advantages of this approach are twofold. MAP provides a more accurate estimate of the GMM parameters compared to standard maximum likelihood estimation (MLE) in the challenging case where the cardinality of the vector set is small. Moreover, there is a correspondence between the Gaussians of two GMMs adapted from a common distribution and one can take advantage of this fact to compute efficiently the probabilistic similarity. This work is applied to the image categorization problem: images are modeled as bags of low-level feature...
Yan Liu, Florent Perronnin
Added 07 Dec 2010
Updated 07 Dec 2010
Type Conference
Year 2008
Where CVPR
Authors Yan Liu, Florent Perronnin
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