A Model for Saliency Detection Using NMFsc Algorithm

Abstract

Saliency mechanism has been considered crucial in the human visual system and helpful to object detection and recognition. This paper addresses an information theoretic model for visual saliency detection. It consists of two steps: first, using the Non-negative Matrix Factorization with sparseness constraints (NMFsc) algorithm to learn the basis functions from a set of randomly sampled natural image patches; and then, applying information theoretic principle to generate the saliency map by the Salient Information (SI) which is calculated from the coefficients represented by basis functions. We compare our model with the previous methods on natural images. Experimental results show that our model performs better than existing approaches.