Detection of Salient Region by Local Spatial Support & High Dimensional Color Transform

Abstract

Automatic salient object regions detection across images, without any prior information or knowledge of the contents of the corresponding images, enhances many computer vision and computer graphics applications. Our approach consists of global and local features, which complement each other to compute a saliency map. The proposed approach automatically detects salient regions in an image dataset. The first key idea of our work is to create a saliency map of an image by using a linear combination of colors in a high dimensional color space. This is based on an observation that salient regions often have distinctive colors compared with backgrounds in human perception, however human perception is complicated and highly nonlinear. By mapping the low dimensional red, green and blue color to a feature vector in a high dimensional color space, we will show that we can composite an accurate saliency map by finding the optimal linear combination of color coefficients in the high dimensional color space. To further improve the performance of our saliency estimation, our second key idea is to utilize relative location and color contrast between super pixels as features and to resolve the saliency estimation from a trimap via a learning based algorithm. The additional local features and learning based algorithm complement the global estimation from the high dimensional color transform based algorithm.