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Video copy detection: a comparative study

Published: 09 July 2007 Publication History

Abstract

This paper presents a comparative study of methods for video copy detection. Different state-of-the-art techniques, using various kinds of descriptors and voting functions, are described: global video descriptors, based on spatial and temporal features; local descriptors based on spatial, temporal as well as spatio-temporal information. Robust voting functions is adapted to these techniques to enhance their performance and to compare them. Then, a dedicated framework for evaluating these systems is proposed. All the techniques are tested and compared within the same framework, by evaluating their robustness under single and mixed image transformations, as well as for different lengths of video segments. We discuss the performance of each approach according to the transformations and the applications considered. Local methods demonstrate their superior performance over the global ones, when detecting video copies subjected to various transformations.

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    cover image ACM Conferences
    CIVR '07: Proceedings of the 6th ACM international conference on Image and video retrieval
    July 2007
    655 pages
    ISBN:9781595937339
    DOI:10.1145/1282280
    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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    Published: 09 July 2007

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    • (2024)Local Deep Learning Quantization for Approximate Nearest Neighbor SearchProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3657615(1125-1129)Online publication date: 30-May-2024
    • (2023)Self-Supervised Video Similarity Learning2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW59228.2023.00504(4756-4766)Online publication date: Jun-2023
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