Common techniques represent images by quantizing local descriptors and summarizing their distribution in a histogram. In this paper we propose to employ a parametric description and compare its capabilities to histogram based approaches. We use the multivariate Gaussian distribution, applied over the SIFT descriptors, extracted with dense sampling on a spatial pyramid. Every distribution is converted to a high-dimensional descriptor, by concatenating the mean vector and the projection of the covariance matrix on the Euclidean space tangent to the Riemannian manifold. Experiments on Caltech-101 and ImageCLEF2011 are performed using the Stochastic Gradient Descent solver, which allows to deal with large scale datasets and high dimensional feature spaces.
Modeling Local Descriptors with Multivariate Gaussians for Object and Scene Recognition / Serra, Giuseppe; Grana, Costantino; Manfredi, Marco; Cucchiara, Rita. - ELETTRONICO. - (2013), pp. 709-712. (Intervento presentato al convegno 21th International Conference on Multimedia (ACM Multimedia 2013) tenutosi a Barcelona, Catalunya, Spain nel Oct 21-25) [10.1145/2502081.2502185].