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TASC: A Transformation-Aware Soft Cascading Approach for Multimodal Video Copy Detection by Yonghong Tian; Mengren Qian; Tiejun Huang is a Computer Science article available to read on EtoBox.

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How to precisely and efficiently detect near-duplicate copies with complicated audiovisual transformations from a large-scale video database is a challenging task. To cope with this challenge, this article proposes a transformation-aware soft cascading (TASC) approach for multimodal video copy detection. Basically, our approach divides query videos into some categories and then for each category designs a __transformation-aware__ chain to organize several detectors in a cascade structure. In each chain, efficient but simple detectors are placed in the forepart, whereas effective but complex detectors are located in the rear. To judge whether two videos are near-duplicates, a Detection-on-Copy-Units mechanism is introduced in the TASC, which makes the decision of copy detection depending on the similarity between their most similar fractions, called __copy units__ (CUs), rather than the video-level similarity. Following this, we propose a CU search algorithm to find a pair of CUs from two videos and a CU-based localization algorithm to find the precise locations of their copy segments that are with the asserted CUs as the center. Moreover, to address the problem that the copies and

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Author

Yonghong Tian; Mengren Qian; Tiejun Huang

Publisher

Association for Computing Machinery; Association for Computing Machinery (ACM) (ISSN 1046-8188)

Published

2015

Language

EN

Field

Computer Science (Physical Sciences)