标题:A 3D-CNN Based Video Hashing Method
作者:Qi, Haifeng; Li, Jing; Wu, Qiang; Wan, Wenbo; Sun, Jiande
通讯作者:Sun, JD;Sun, Jiande
作者机构:[Qi, Haifeng; Wu, Qiang] Shandong Univ, Sch Informat Sci & Engn, Jinan, Shandong, Peoples R China.; [Li, Jing] Shandong Management Univ, Sch Mech & 更多
会议名称:10th International Conference on Digital Image Processing (ICDIP)
会议日期:MAY 11-14, 2018
来源:TENTH INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING (ICDIP 2018)
出版年:2018
卷:10806
DOI:10.1117/12.2502933
关键词:Video Retrieval; 3D-CNN; video feature; video hashing
摘要:Most video hashing algorithms have the common pipeline, which consists of feature extraction and hash mapping. The performance of video hash is usually promoted via the improvement in one or both of aspects. In this paper, a learning-based video feature is used, which is obtained via a 3D-CNN model. The 3DCNN-based features can represent both spatial and temporal information of videos, as 3D convolutions used in 3DCNN can capture the motion information through multiple adjacent frames. A video hashing algorithm is proposed based on the 3DCNN-based feature, which is defined as CNNF. In addition, the hash length optimization method is used to get the approximately optimal hash length in hash mapping stage of the proposed algorithm. Since the feature extraction and hash length optimization are independent to hash mapping algorithms, several classical hashing algorithms are adopted to verify the improvements of these two aspects via the video copy detection task. Experiments demonstrate the performance of the proposed CNNF-Hash algorithm.
收录类别:CPCI-S;EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85052638659&doi=10.1117%2f12.2502933&partnerID=40&md5=11f3648843374c54470e6546146a7dc9
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