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

DOI HANDLE Web Site 被引用文献1件 参考文献28件 オープンアクセス

抄録

<p>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.</p>

収録刊行物

被引用文献 (1)*注記

もっと見る

参考文献 (28)*注記

もっと見る

キーワード

詳細情報 詳細情報について

問題の指摘

ページトップへ