Object-Based Visual Saliency via Laplacian Regularized Kernel Regression

Saliency object detection has been a very active research topic recently, due to its extensive applications in image compression, scene understanding, image retrieval, and so forth. The overwhelming majority of existing computational models are designed based on computer vision techniques by using a lot of image cues and priors. In fact, salient object detection is derived from the biological perceptual mechanism, and biological evidence shows that the object-based saliency stems from the spread of the spatial attention. Inspired by this, we attempt to utilize the emerging spread mechanism of object attention to construct a new computational model. A novel Laplacian regularized kernel regression diffusion model is proposed to fulfill the spread process. The proposed diffusion model, which is able to fully capture both global and local structures of the image, thereby allows for effective propagation of spatial attention with visual grouping cues, yielding a well-structured object-based saliency map. Experimental results demonstrate that our method can achieve encouraging performance in comparison with the state-of-the-art methods.