Image Classification Using a Mixture of Subspace Models

Abstract

This paper introduces a novel method for image classification using local feature descriptors. The method utilizes linear subspaces of local descriptors for characterizing their distribution and extracting image features. The extracted features are transformed into more discriminative features by the linear discriminant analysis and employed for recognizing their categories. Experimental results demonstrate that this method is competitive with the Fisher kernel method in terms of classification accuracy.