Scene Image Classification Using Reduced Virtual Feature Representation in Sparse Framework

Abstract

In this paper, we address the task of scene image classification in sparse framework. Recent scene image datasets consist of thousands of different size images with size of the order of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> pixels. Motivated by the fact that every image has a different size, we propose a dynamic kernel <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> which works over set of feature maps obtained for an image from last convolutional pooling layer of a pre-trained CNN. The size of feature maps depends on the input image size leading to the requirement of a dynamic kernel to compute similarity score between feature maps of different images. The kernel matrix obtained by using a dynamic kernel is large in size owing to the large number of training examples. To handle this we propose to use the concept of reduced virtual features (RVFs) obtained by diagonalizing the kernel matrix. RVF is a fixed length representation of a scene image irrespective of its true size. Classification is done in sparse framework by applying block sparsity constraint over sparse coefficients using dictionary built from RVFs. The proposed approach tested over standard datasets like Vogel-Schiele, MIT-8, MIT-67 and SUN-397 yields good results.