Exponential discriminant regularization using Nonnegative constraint and image descriptor

Recently, various clustering approaches were proposed that incorporated both local and global information in an image dataset to learn nonlinear manifold. However, we have to calibrate a number of clustering parameters in these clustering models. In this study, we propose novel less-parameterized Exponential Discriminative Regularization with Nonnegative constraint (NESDR) clustering model based on exponential discriminant analysis in which the matrix singularity problem of discriminant analysis is handled by projecting scatter matrices in exponential domain. Further, we observed that well separated images at local level cannot be achieved using pixel value based image features for challenging image databases that contain images with different expressions, pose, illumination, or occlusion variations. We used Gist image features with proposed NEDSR model. We compared clustering performance of NEDSR-Gist with state-of-the-art existing clustering models such as Kmeans, discriminative Kmeans, normalized cut, local discriminant model and global integration, and NSDR. We report significant overall performance improvement of 8.9% (clustering accuracy) and 10.8% (normalized mutual information) on 6 benchmark image datasets over near competitor NSDR model.