Concept Level Discriminant Analysis Techniques for Dimension Reduction in Image Classification Tasks

Abstract

A limitation of using the discriminant analysis techniques for dimension reduction in image classification tasks is that the number of classes is significantly smaller than the dimension of global feature vectors used to represent the images. In such cases, the reduced dimension is restricted by the number of classes and that reduced dimension may not adequately capture the necessary discriminatory information for classification. We propose a method to perform the discriminant analysis using the concept level labels for local feature vectors extracted from the blocks of an image. The dimension reduction is carried out on the local feature vectors. We consider the indices of the clusters of local feature vectors of all the images as the unnamed concept level labels. As the number of concept level labels is larger than the number of class level labels, the reduced dimension for local feature vectors can be higher than the number of class level labels. We consider the Gaussian mixture model based classifiers and support vector machine based classifiers for image classification using the set of local feature vectors representation of images. Results of experimental studies on image classification for MIT-8 and Vogel-6 image datasets demonstrate the effectiveness of proposed concept level discriminant analysis techniques for dimension reduction.