An algorithm for detecting multiple salient objects in images via adaptive feature selection
This paper presents an algorithm for detecting multiple salient objects in images. The algorithm extends our previous algorithm which was designed to detect only a single salient object. The new algorithm employs five feature maps (lightness distance, color distance, contrast, sharpness, and edge strength), along with a new image-adaptive technique for estimating the usefulness of each feature map based on a local measure of cluster density. As we will demonstrate, our new version can successfully detect multiple salient objects on images for which the previous version did not succeed. Testing on subsets of images from two databases shows that the proposed algorithm performs well on a variety of images containing multiple salient objects.