The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection of video sequences using compact signatures
ACM Transactions on Information Systems (TOIS)
Graph mining: Laws, generators, and algorithms
ACM Computing Surveys (CSUR)
Video search reranking via information bottleneck principle
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Fast tracking of near-duplicate keyframes in broadcast domain with transitivity propagation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Video copy detection: a comparative study
Proceedings of the 6th ACM international conference on Image and video retrieval
Statistical summarization of content features for fast near-duplicate video detection
Proceedings of the 15th international conference on Multimedia
Video search re-ranking via multi-graph propagation
Proceedings of the 15th international conference on Multimedia
Practical elimination of near-duplicates from web video search
Proceedings of the 15th international conference on Multimedia
Video search reranking through random walk over document-level context graph
Proceedings of the 15th international conference on Multimedia
UQLIPS: a real-time near-duplicate video clip detection system
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
CSV: visualizing and mining cohesive subgraphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Near-duplicate keyframe retrieval by nonrigid image matching
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Scalable mining of large video databases using copy detection
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Bayesian video search reranking
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Modeling video hyperlinks with hypergraph for web video reranking
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Bounded coordinate system indexing for real-time video clip search
ACM Transactions on Information Systems (TOIS)
Continuous Content-Based Copy Detection over Streaming Videos
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Understanding near-duplicate videos: a user-centric approach
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Scalable detection of partial near-duplicate videos by visual-temporal consistency
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Scale-rotation invariant pattern entropy for keypoint-based near-duplicate detection
IEEE Transactions on Image Processing
Multimedia search with pseudo-relevance feedback
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Pushing tougher constraints in frequent pattern mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Correlation-based retrieval for heavily changed near-duplicate videos
ACM Transactions on Information Systems (TOIS)
Discovering areas of interest with geo-tagged images and check-ins
Proceedings of the 20th ACM international conference on Multimedia
Near-duplicate video retrieval: Current research and future trends
ACM Computing Surveys (CSUR)
ACM Transactions on Information Systems (TOIS)
Hi-index | 0.00 |
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.