A Novel Probabilistic Contrast-Based Complex Salient Object Detection

Abstract

Saliency computation has wide applications. It is now being used in almost all vision-related applications. But, identifying saliency is still a problem. Various computational models have been proposed for identifying saliency. Global contrast-based method is used extensively. This method computes the contrast by measuring the color difference between image and specified region. It produces saliency with non-salient points, but, in the process, it loses some structural and spatial information. To address these limitations, the proposed method, i.e., Poisson-based probabilistic contrast, produces saliency with the concave topographical surface. This surface encloses the prominent object with all its structural and spatial information, or with all the salient features. Then, it is used as a reference plane for regional depth, color and spatial saliency integration. The proposed method has three stages. In the first stage, a probabilistic contrast is computed using Poisson-based maximum likelihood estimation by addition of chrominance and luminance contrast. The luminance contrast is normalized by proposed “enhance and suppress luminance method.” In the second stage, the regional color, depth, and spatial saliencies are integrated into the topographical surface to enhance the saliency. In the third and last stages, i.e., saliency enhancement stage, central saliency is used on global color distinction. The proposed method is evaluated on the publicly available datasets. Their performance is compared with 12 state-of-the-art methods. The experimental result presented here shows that the proposed method performs better. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.