Efficient supervised dimensionality reduction for image categorization

This paper addresses the problem of large scale image representation for object recognition and classification. Our work deals with the problem of optimizing the classification accuracy and the dimensionality of the image representation. We propose to iteratively select sets of projections from an external dataset, using Bagging and feature selection thanks to SVM normals. Features are selected using weights of SVM normals in orthogonalized sets of projections. The Bagging strategy is employed to improve the results and provide more stable selection. The overall algorithm linearly scales with the size of features, and thus is able to process the large state-of-the-art image representation. Given Spatial Fisher Vectors as input, our method consistently improves the classification accuracy for smaller vector dimensionality, as demonstrated by our results on the popular and challenging PASCAL VOC 2007 benchmark.