Multi-scale point set saliency detection based on site entropy rate

Abstract

Visual saliency in images has been studied extensively in many literatures, but there is no much work on point sets. In this paper, we propose an approach based on pointwise site entropy rate to detect the saliency distribution in unorganized point sets and range data, which are lack of topological information. In our model, a point set is first transformed to a sparsely-connected graph. Then the model runs random walks on the graphs to simulate the signal/information transmission. We evaluate point saliency using site entropy rate (SER), which reflects average information transmitted from a point to its neighbors. By simulating the diffusion process on each point, multi-scale saliency maps are obtained. We combine the multi-scale saliency maps to generate the final result. The effectiveness of the proposed approach is demonstrated by comparisons to other approaches on a range of test models. The experiment shows our model achieves good performance, without using any connectivity information.