Journal of Guangxi Normal University(Natural Science Edition) ›› 2022, Vol. 40 ›› Issue (5): 72-89.doi: 10.16088/j.issn.1001-6600.2022022101

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Survey of Video Hash Research Based on Hand-craft Features

YU Mengzhu, TANG Zhenjun*   

  1. Guangxi Key Lab of Multi-source Information Mining and Security (Guangxi Normal University), Guilin Guangxi 541004, China
  • Received:2022-02-21 Revised:2022-04-13 Online:2022-09-25 Published:2022-10-18

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Abstract: Video hash is a short digital sequence based on visual content extracted from video. In practical applications, the strategy of representing a video by its video hash can reduce storage cost of video and complexity of calculating video similarity. Video hash is widely used in many applications, such as copy detection, tampering forensics, video indexing, and video retrieval, etc. In recent years, there are many progresses on video hashing research. Researchers have designed and developed some techniques of hand-craft feature extraction and presented many video hashing algorithms. This paper classifies the hand-craft features based video hashing algorithms into two categories: spatial computation, and spatial-temporal computation. Moreover, the video hashing algorithms based on spatial computation are further divided into two sub-categories: frame-by-frame calculation and key frame calculation. The video hashing algorithms based on spatial-temporal computation are further divided into five sub-categories: orthogonal transform, statistical feature, visual feature point, data dimensionality reduction and other technique. In this paper, some typical algorithms of each category are first described, and their performances are then summarized. Next, the common-used metrics of hash similarity, performance evaluation indices and video datasets are introduced. Finally, the future trend of research is presented, including video hashing for tampering forensics, efficient video hashing based on deep learning, lightweight video hashing for mobile applications, and so on.

Key words: video hashing, hand-craft features, feature extraction, key frame, data dimensionality reduction

CLC Number: 

  • TP391.41
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