Proceedings ArticleDOI
Efficient video retrieval by locality sensitive hashing
Shiyan Hu
- Vol. 2, pp 449-452
TLDR
In this paper, a new scheme for fast video retrieval is proposed, in the scheme, a video is represented by a set of feature vectors which are computed using the robust alpha-trimmed average color histogram to efficiently retrieve videos.Abstract:
In this paper, a new scheme for fast video retrieval is proposed. In the scheme, a video is represented by a set of feature vectors which are computed using the robust alpha-trimmed average color histogram. To efficiently retrieve videos, the locality sensitive hashing technique, which involves a uniform distance shrinking projection, is applied. Such a technique does not suffer from the notorious "curse of dimensionality" problem in handling high-dimensional data point sets and guarantees that geometrically close vectors are hashed to the same bucket with high probability. In addition, unlike the conventional techniques, the involved similarity measure incorporates the temporal order of video sequences. The experimental results demonstrate that the proposed scheme outperforms the conventional approaches in accuracy and efficiency.read more
Citations
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Proceedings ArticleDOI
Video fingerprinting for copy identification: from research to industry applications
TL;DR: A comprehensive review of video fingerprinting technology and its applications in identifying, tracking, and managing copyrighted content on the Internet and an overview of a number of industry-driven applications that rely onVideo fingerprinting.
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A probabilistic molecular fingerprint for big data settings
Daniel Probst,Jean-Louis Reymond +1 more
TL;DR: A new fingerprint, called MinHash fingerprint, up to six bonds (MHFP6), which encodes detailed substructures using the extended connectivity principle of ECFP in a fundamentally different manner, increasing the performance of exact nearest neighbor searches in benchmarking studies and enabling the application of locality sensitive hashing (LSH) approximate nearest neighbor search algorithms.
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Locality-Sensitive IoT Network Traffic Fingerprinting for Device Identification
Batyr Charyyev,Mehmet Hadi Gunes +1 more
TL;DR: This article introduces a novel approach to identify an IoT device based on the locality-sensitive hash of its traffic flow, which achieves precision and recall above 90% on average and performs equally well compared to the state-of-the-art machine learning-based methods.
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An Empirical Study on Large-Scale Content-Based Image Retrieval
TL;DR: This paper proposes a scalable content-based image retrieval scheme using locality-sensitive hashing (LSH), and conducts extensive evaluations on a large image testbed of a half million images, which is promising for building Web-scale CBIR systems.
Proceedings ArticleDOI
Local summarization and multi-level LSH for retrieving multi-variant audio tracks
TL;DR: Experimental evaluations performed on a real multi-variant audio dataset confirm that the approach relying on local summarization and multi-level Locality-Sensitive Hashing supports fast and reliable retrieval of audio track variants.
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