Ant-Inspired Visual Saliency Detection in Image

Visual saliency detection has been of great research interest in recent years, since it is potential for a wide range of applications, such as object detection, content-based image retrieval and perceptual image compression. Human perceptual attention usually tends to firstly pick attended regions, which correspond to prominent objects in an image, rather than the whole image (Jams, 1890; Itti, 2000). Visual saliency detection aims to simulate the behavior of the human visual system (HVS) by automatically producing saliency maps of the image. Much research has been done on modeling visual attention; they can be classified into the following two categories: bottom-up and top-down. The first approach is an image-driven approach to select visual information based on saliency in the image itself, while the second one is a goal-driven approach based on both a user-defined task and the image itself. This chapter is focused on the first kind of approach. In general, it uses certain means of determining local contrast of image regions with their surroundings using one or more of the features of color, intensity, and orientation. Usually, separate feature maps are created for each of the features used and then combined to obtain the final saliency AbsTRACT