Ranking the local invariant features for the robust visual saliencies

Local invariant feature based methods have been proven to be effective in computer vision for object recognition and learning. But for an image, the number of points detected and to be matched may be very large, or even redundantly represent the shape information present. Since selective attention is a basic mechanism of the visual system, we explore whether there is a subset of salient points that can be robustly detected and matched. We propose a method to rank the redundant local invariant features. The results prove that the top ranked points capture the salient information effectively. The method can be used as a pre-processing step for the bag-of-feature based methods or graph based methods. Here they simplify the complexity of the processes, such as training, matching and tracking.