Open AccessImproving the robustness of motion vector temporal descriptor
Author(s) -
Rahmani Farzaneh,
Zargari Farzad,
Ghanbari Mohammad
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.0206
Subject(s) - search engine indexing , computer science , robustness (evolution) , artificial intelligence , motion vector , histogram , pattern recognition (psychology) , computer vision , motion estimation , feature vector , motion (physics) , feature (linguistics) , image (mathematics) , biochemistry , gene , chemistry , linguistics , philosophy
Motion vectors (MVs) are the most common temporal descriptors in video analysis, indexing and retrieval applications. However, video indexing and analysis based on MVs do not perform well for videos at different dimension ratios (DRs) or even various resolutions. As a result, video indexing and analysis which are based on identifying similar video face with many difficulties at different DRs or resolutions by MVs. In this study, a two‐stage algorithm is introduced to make MV descriptors robust against variations first in DR and then at resolution. In the experiments performed on motion vector histograms, the proposed method improves the performance on identifying similar videos at various spatial specifications by up to 73%. Moreover, in the video retrieval experiments, the proposed modified MV outperforms original MV feature vector. This is an indication of improvement in differentiation of similar and dissimilar videos by the proposed temporal feature vector.