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Graph-Based Video Search Reranking with Local and Global Consistency Analysis

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Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/70424

Title: Graph-Based Video Search Reranking with Local and Global Consistency Analysis
Authors: YOSHIDA, Soh Browse this author
OGAWA, Takahiro Browse this author →KAKEN DB
HASEYAMA, Miki Browse this author →KAKEN DB
MUNEYASU, Mitsuji Browse this author
Keywords: video search reranking
graph learning
graph consistency analysis
spectral clustering
Issue Date: May-2018
Publisher: IEICE, the Institute of Electronics, Information and Communication Engineers
Journal Title: IEICE Transactions on Information and Systems
Volume: E101.D
Issue: 5
Start Page: 1430
End Page: 1440
Publisher DOI: 10.1587/transinf.2017EDP7277
Abstract: Video reranking is an effective way for improving the retrieval performance of text-based video search engines. This paper proposes a graph-based Web video search reranking method with local and global consistency analysis. Generally, the graph-based reranking approach constructs a graph whose nodes and edges respectively correspond to videos and their pairwise similarities. A lot of reranking methods are built based on a scheme which regularizes the smoothness of pairwise relevance scores between adjacent nodes with regard to a user’s query. However, since the overall consistency is measured by aggregating only the local consistency over each pair, errors in score estimation increase when noisy samples are included within query-relevant videos’ neighbors. To deal with the noisy samples, the proposed method leverages the global consistency of the graph structure, which is different from the conventional methods. Specifically, in order to detect this consistency, the propose method introduces a spectral clustering algorithm which can detect video groups, in which videos have strong semantic correlation, on the graph. Furthermore, a new regularization term, which smooths ranking scores within the same group, is introduced to the reranking framework. Since the score regularization is performed by both local and global aspects simultaneously, the accurate score estimation becomes feasible. Experimental results obtained by applying the proposed method to a real-world video collection show its effectiveness.
Rights: Copyright ©2018 The Institute of Electronics, Information and Communication Engineers
Relation: https://search.ieice.org/
Type: article
URI: http://hdl.handle.net/2115/70424
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 小川 貴弘

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