Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval

Gengshen Wu, Jungong Han, Yuchen Guo, Li Liu, Guiguang Ding, Qiang Ni, Ling Shao

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

118 Dyfyniadau(SciVal)
50 Wedi eu Llwytho i Lawr (Pure)


This paper proposes a deep hashing framework, namely Unsupervised Deep Video Hashing (UDVH), for largescale video similarity search with the aim to learn compact yet effective binary codes. Our UDVH produces the hash codes in a self-taught manner by jointly integrating discriminative video representation with optimal code learning, where an efficient alternating approach is adopted to optimize the objective function. The key differences from most existing video hashing methods lie in 1) UDVH is an unsupervised hashing method that generates hash codes by cooperatively utilizing feature clustering and a specifically-designed binarization with the original neighborhood structure preserved in the binary space; 2) a specific rotation is developed and applied onto video features such that the variance of each dimension can be balanced, thus facilitating the subsequent quantization step. Extensive experiments performed on three popular video datasets show that UDVH is overwhelmingly better than the state-of-the-arts in terms of various evaluation metrics, which makes it practical in real-world applications.

Iaith wreiddiolSaesneg
Rhif yr erthygl8540456
Tudalennau (o-i)1993-2007
Nifer y tudalennau15
CyfnodolynIEEE Transactions on Image Processing
Rhif cyhoeddi4
Dyddiad ar-lein cynnar19 Tach 2018
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 30 Ebr 2019
Cyhoeddwyd yn allanolIe

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