Efficient Mining of Repetitions in Large-Scale TV Streams with Product Quantization Hashing
Abstract
Duplicates or near-duplicates mining in video sequences is of broad interest to many multimedia applications. How to design an effective and scalable system, however, is still a challenge to the community. In this paper, we present a method to detect recurrent sequences in large-scale TV streams in an unsupervised manner and with little a priori knowledge on the content. The method relies on a product k -means quantizer that efficiently produces hash keys adapted to the data distribution for frame descriptors. This hashing technique combined with a temporal consistency check allows the detection of meaningful repetitions in TV streams. When considering all frames (about 47 millions) of a 22-day long TV broadcast, our system detects all repetitions in 15 minutes, excluding the computation of the frame descriptors. Experimental results show that our approach is a promising way to deal with very large video databases.
Cite
Text
Yuan et al. "Efficient Mining of Repetitions in Large-Scale TV Streams with Product Quantization Hashing." European Conference on Computer Vision, 2012. doi:10.1007/978-3-642-33863-2_27Markdown
[Yuan et al. "Efficient Mining of Repetitions in Large-Scale TV Streams with Product Quantization Hashing." European Conference on Computer Vision, 2012.](https://mlanthology.org/eccv/2012/yuan2012eccv-efficient/) doi:10.1007/978-3-642-33863-2_27BibTeX
@inproceedings{yuan2012eccv-efficient,
title = {{Efficient Mining of Repetitions in Large-Scale TV Streams with Product Quantization Hashing}},
author = {Yuan, Jiangbo and Gravier, Guillaume and Campion, Sébastien and Liu, Xiuwen and Jégou, Hervé},
booktitle = {European Conference on Computer Vision},
year = {2012},
pages = {271-280},
doi = {10.1007/978-3-642-33863-2_27},
url = {https://mlanthology.org/eccv/2012/yuan2012eccv-efficient/}
}