ACM Computing Surveys (CSUR)
Towards effective indexing for very large video sequence database
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Hierarchical Indexing Structure for Efficient Similarity Search in Video Retrieval
IEEE Transactions on Knowledge and Data Engineering
Efficient similarity search by summarization in large video database
ADC '07 Proceedings of the eighteenth conference on Australasian database - Volume 63
Bounded coordinate system indexing for real-time video clip search
ACM Transactions on Information Systems (TOIS)
Interactive near-duplicate video retrieval and detection
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Efficient video indexing scheme for content-based retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Efficient video similarity measurement with video signature
IEEE Transactions on Circuits and Systems for Video Technology
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Videos have become one of the most important communication means these days. In this paper, we propose an approach to efficiently bulk-insert a set of new video index-entries into the existing video database for content-based video search. Given the current situation that enormous amount of new videos are created and uploaded to the video sharing websites, the efficient approaches are highly required. The environment we focused is where a B+-tree is applied to index the video content-features. We propose a hybrid bulk-insertion approach based on a well-known bulk-insertion. Unlike the traditional bulk-insertion in which the traversals to insert the remaining index entries are performed to the ancestors, we propose to add a leaf-level traversal to improve the efficiency. Thus, our approach works in a hybrid manner, i.e., it switches between the leaf and ancestor traversals with regard to a condition with a very small additional cost. The experiments have been conducted to evaluate our proposed work by comparing to the one-by-one insertion approach, and the traditional bulk-insertion approach. The experiment results show that the proposed approach is highly efficient for video content-based indexing.