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Compact and low-complexity binary feature descriptor and Fisher Vectors for video analytics
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Leyva, Roberto, Sanchez Silva, Victor and Li, Chang-Tsun (2019) Compact and low-complexity binary feature descriptor and Fisher Vectors for video analytics. IEEE Transactions on Image Processing, 28 (12). pp. 6169-6184. doi:10.1109/TIP.2019.2922826
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Official URL: https://doi.org/10.1109/TIP.2019.2922826
Abstract
In this paper, we propose a compact and low- complexity binary feature descriptor for video analytics. Our binary descriptor encodes the motion information of a spatio- temporal support region into a low-dimensional binary string. The descriptor is based on a binning strategy and a construction that binarizes separately the horizontal and vertical motion components of the spatio-temporal support region. We pair our descriptor with a novel Fisher Vector (FV) scheme for binary data to project a set of binary features into a fixed length vector in order to evaluate the similarity between feature sets. We test the effectiveness of our binary feature descriptor with FVs for action recognition, which is one of the most challenging tasks in computer vision, as well as gait recognition and animal behavior clustering. Several experiments on the KTH, UCF50, UCF101, CASIA-B, and TIGdog datasets show that the proposed binary feature descriptor outperforms the state-of-the-art feature descriptors in terms of computational time and memory and stor- age requirements. When paired with FVs, the proposed feature descriptor attains a very competitive performance, outperforming several state-of-the-art feature descriptors and some methods based on convolutional neural networks.
| Item Type: | Journal Article | ||||||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||||||||||
| Divisions: | Faculty of Science > Computer Science | ||||||||||||
| Library of Congress Subject Headings (LCSH): | Optical pattern recognition , Digital video , Pattern perception , Pattern recognition systems | ||||||||||||
| Journal or Publication Title: | IEEE Transactions on Image Processing | ||||||||||||
| Publisher: | IEEE | ||||||||||||
| ISSN: | 1057-7149 | ||||||||||||
| Official Date: | December 2019 | ||||||||||||
| Dates: |
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| Date of first compliant deposit: | 7 October 2019 | ||||||||||||
| Volume: | 28 | ||||||||||||
| Number: | 12 | ||||||||||||
| Page Range: | pp. 6169-6184 | ||||||||||||
| DOI: | 10.1109/TIP.2019.2922826 | ||||||||||||
| Status: | Peer Reviewed | ||||||||||||
| Publication Status: | Published | ||||||||||||
| Publisher Statement: | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||||||
| Grant number: | IDENTITY, Project ID: 690907 | ||||||||||||
| RIOXX Funder/Project Grant: |
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