Fisher Vector based on Full-covariance Gaussian Mixture Model
Masayuki Tanaka, Akihiko Torii, Masatoshi Okutomi · IPSJ Transactions on Computer Vision and Applications · 2013
In image retrieval applications, the Fisher vector of the Gaussian mixture model (GMM) with a diagonalcovariance structure is known as a powerful tool to describe an image by aggregating local descriptors extracted from the image.In this paper, we propose the Fisher vector of the GMM with a full-covariance structure.The closed-form approximation of the GMM with a full-covariance structure is derived.Our observation is that the Fisher vector of a higher dimensional GMM yields higher image retrieval performance.The Fisher vector for the GMM with a blockdiagonal-covariance structure is also introduced to provide moderate dimensionality for the GMM.Experimental comparisons performed using two major datasets demonstrate that the proposed Fisher vector outperforms state-of-the-art algorithms.