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Mining near-duplicate graph for cluster-based reranking of web video search results

Published: 23 November 2010 Publication History

Abstract

Recently, video search reranking has been an effective mechanism to improve the initial text-based ranking list by incorporating visual consistency among the result videos. While existing methods attempt to rerank all the individual result videos, they suffer from several drawbacks. In this article, we propose a new video reranking paradigm called cluster-based video reranking (CVR). The idea is to first construct a video near-duplicate graph representing the visual similarity relationship among videos, followed by identifying the near-duplicate clusters from the video near-duplicate graph, then ranking the obtained near-duplicate clusters based on cluster properties and intercluster links, and finally for each ranked cluster, a representative video is selected and returned. Compared to existing methods, the new CVR ranks clusters and exhibits several advantages, including superior reranking by utilizing more reliable cluster properties, fast reranking on a small number of clusters, diverse and representative results. Particularly, we formulate the near-duplicate cluster identification as a novel maximally cohesive subgraph mining problem. By leveraging the designed cluster scoring properties indicating the cluster's importance and quality, random walk is applied over the near-duplicate cluster graph to rank clusters. An extensive evaluation study proves the novelty and superiority of our proposals over existing methods.

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      cover image ACM Transactions on Information Systems
      ACM Transactions on Information Systems  Volume 28, Issue 4
      November 2010
      204 pages
      ISSN:1046-8188
      EISSN:1558-2868
      DOI:10.1145/1852102
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Publication History

      Published: 23 November 2010
      Accepted: 01 April 2010
      Revised: 01 February 2010
      Received: 01 September 2009
      Published in TOIS Volume 28, Issue 4

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      Author Tags

      1. Cluster-based video reranking
      2. Web search
      3. graph mining near-duplicate

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      • (2020)A supervised deep convolutional based bidirectional long short term memory video hashing for large scale video retrieval applicationsDigital Signal Processing10.1016/j.dsp.2020.102729102(102729)Online publication date: Jul-2020
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