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Smooth Neighborhood Structure Mining on Multiple Affinity Graphs with Applications to Context-Sensitive Similarity
Song Bai; Shaoyan Sun; Xiang Bai; Zhaoxiang Zhang; Qi Tian
2016-10-11
Conference NameThe 14th European Conference on Computer Vision
Source PublicationECCV 2016
Conference DateOctober 11-14, 2016
Conference PlaceAmsterdam, The Netherlands
AbstractDue to the ability of capturing geometry structures of the data manifold, diffusion process has demonstrated impressive performances in retrieval task by spreading the similarities on the affinity graph. In view of robustness to noise edges, diffusion process is usually localized, i.e., only propagating similarities via neighbors. However, selecting neighbors smoothly on graph-based manifolds is more or less ignored by previous works. In this paper, we propose a new algorithm called Smooth Neighborhood (SN) that mines the neighborhood structure to satisfy the manifold assumption. By doing so, nearby points on the underlying manifold are guaranteed to yield similar neighbors as much as possible. Moreover, SN is adjusted to tackle multiple affinity graphs by imposing a weight learning paradigm, and this is the primary difference compared with related works which are only applicable with one affinity graph. Exhausted experimental results and comparisons against other algorithms manifest the effectiveness of the proposed algorithm.
KeywordDiffusion Process Image/shape Retrieval Affinity Graph
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13250
Collection类脑智能研究中心
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
GB/T 7714
Song Bai,Shaoyan Sun,Xiang Bai,et al. Smooth Neighborhood Structure Mining on Multiple Affinity Graphs with Applications to Context-Sensitive Similarity[C],2016.
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