Complex Salient Regions for Computer Vision Problems

Sergio Escalera, Petia Radeva, Oriol Pujol

Abstract: The good of interest point detectors is to find, in an unsupervised way, keypoints easy to extract and at the same time robust to image transformations. We present a novel set of saliency feathers based on image singularities that takes into account the region content in terms of intensity and local structure. The region complexity is estimated by means of the entropy of the grey-level information; shape information is obtained by measuring the entropy of significant orientations. The regions are located in their representative scale and categorized by their complexity level. Thus, the regions are highly discriminable and less sensitive to confusion and false alalarm than the traditional approaches. We compare the novel complex salient regions with the state-of-the-art keypoint detectors. The presented interest points show robustness to a wide set of image transformations and high repeatability, as well as allows matching from different camera points of view. Beside. We show the temporal robustness of the novel salient regions in real video sequences, being potentially useful for matching, image retrieval, and object categorization problems.