TY - GEN
T1 - Multi-scale point set saliency detection based on site entropy rate
AU - Guo, Yu
AU - Wang, Fei
AU - Liu, Pengyu
AU - Xin, Jingmin
AU - Zheng, Nanning
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85007154305
U2 - 10.1007/978-3-319-48890-5_36
DO - 10.1007/978-3-319-48890-5_36
M3 - 会议稿件
AN - SCOPUS:85007154305
SN - 9783319488899
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 366
EP - 375
BT - Advances in Multimedia Information Processing – 17th Pacific-Rim Conference on Multimedia, PCM 2016, Proceedings
A2 - Chen, Enqing
A2 - Tie, Yun
A2 - Gong, Yihong
PB - Springer Verlag
T2 - 17th Pacific-Rim Conference on Multimedia, PCM 2016
Y2 - 15 September 2016 through 16 September 2016
ER -