Patch-based salient region detection using statistical modeling in the non-subsampled contourlet domain

Abstract

A salient region is part of the image that captures the greatest attention by the human visual system. In this paper, we propose a novel salient region detection technique in the non-subsampled contourlet domain. The image is first decomposed into non-overlapping patches in order to fully exploit the repetitive patterns in the image. It is known that the non-subsampled contourlet transform provides an efficient multi-resolution, multi-directional, localized and shift invariant decomposition of images. In view of this, by using the statistical properties of non-subsampled contourlet coefficients of image patches, a set of feature descriptors are extracted to construct the feature map for each color channel. An entropy-based criterion is proposed to combine the channel feature maps into a saliency map. Simulations are conducted on a dataset of natural images to evaluate the performance of the proposed method and to compare it with that of the other existing methods. The results show that the proposed salient region detection method provides higher precision, recall, and F-measure and lower mean absolute error values as compared to the other existing methods.