Rights statement: ©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Accepted author manuscript, 3.51 MB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval
AU - Wu, G.
AU - Han, Jungong
AU - Guo, Y.
AU - Liu, L.
AU - Ding, Guiguang
AU - Ni, Q.
AU - Shao, L.
N1 - ©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - This paper proposes a deep hashing framework, namely, unsupervised deep video hashing (UDVH), for large-scale 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 and 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 the UDVH is overwhelmingly better than the state of the arts in terms of various evaluation metrics, which makes it practical in real-world applications. © 1992-2012 IEEE.
AB - This paper proposes a deep hashing framework, namely, unsupervised deep video hashing (UDVH), for large-scale 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 and 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 the UDVH is overwhelmingly better than the state of the arts in terms of various evaluation metrics, which makes it practical in real-world applications. © 1992-2012 IEEE.
KW - balanced rotation
KW - deep learning
KW - feature representation
KW - similarity retrieval
KW - Video hashing
KW - Codes (symbols)
KW - Hash functions
KW - Optimal systems
KW - Feature clustering
KW - Feature representation
KW - Neighborhood structure
KW - Objective functions
KW - Similarity retrieval
KW - Video representations
KW - Video similarity search
KW - Deep learning
U2 - 10.1109/TIP.2018.2882155
DO - 10.1109/TIP.2018.2882155
M3 - Journal article
VL - 28
SP - 1993
EP - 2007
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
SN - 1057-7149
IS - 4
ER -