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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

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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
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:
DateEvent
December 2019Available
26 June 2019Accepted
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:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIEDMexico.‏ Secretaría de Educación Públicahttp://viaf.org/viaf/139345796
UNSPECIFIEDConsejo Nacional de Ciencia y Tecnologíahttp://dx.doi.org/10.13039/501100003141
690907H2020 European Research Councilhttp://dx.doi.org/10.13039/100010663

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