Multi-scale analysis of color and texture for salient object detection
In this paper we propose a multi-scale segment-based framework for salient object detection. In this framework texture and color features are used together to provide diverse information of salient object. Segmentation is performed on three different scales so that the object boundary can be accurately captured with high probability. Besides, we propose a novel adaptive feature combination mechanism to combine the saliency maps produced with different features, in which the combining weight of each saliency map is learned using online learning. Experiment results demonstrate that the proposed method significantly outperforms the state-of-the-art methods.