Hong X., Xu Y., Zhao G. (2017) LBP-TOP: A Tensor Unfolding Revisit. In: Chen CS., Lu J., Ma KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science, vol 10116. Springer, Cham
LBP-TOP : a tensor unfolding revisit
|Author:||Hong, Xiaopeng1; Xu, Yingyue1; Zhao, Guoying1|
1Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 0.8 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe201902286518
|Publish Date:|| 2018-03-15
Local Binary Pattern histograms from Three Orthogonal Planes (LBP-TOP) has shown its promising performance on facial expression recognition as well as human activity analysis, as it extracts features from spatial-temporal information. Originally, as the calculation of LBP-TOP has to traverse all the pixels in the three dimensional space to compute the LBP operation along XY, YT and XT planes respectively, the frequent use of loops in implementation shapely increases the computational costs. In this work, we aim to fasten the computational efficiency of LBP-TOP on spatial-temporal information and introduce the concept of tensor unfolding to accelerate the implementation process from three-dimensional space to two-dimensional space. The spatial-temporal information is interpreted as a 3-order tensor, and we use tensor unfolding method to compute three concatenated big matrices in two-dimensional space. LBP operation is then performed on the three unfolded matrices. As the demand for loops in implementation is largely down, the computational cost is substantially reduced. We compared the computational time of the original LBP-TOP implementation to that of our fast LBP-TOP implementation on both synthetic and real data, the results show that the fast LBP-TOP implementation is much more time-saving than the original one. The implementation code of the proposed fast LBP-TOP is now publicly available (The implementation code of the proposed fast LBP-TOP can be downloaded at http://www.ee.oulu.fi/research/imag/cmvs/files/code/Fast_LBPTOP_Code.zip).
Lecture notes in computer science
|Pages:||513 - 527|
Computer Vision – ACCV 2016 Workshops : ACCV 2016 International Workshops, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part I
|Host publication editor:||
Asian Conference on Computer Vision
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
This work is sponsored by the Academy of Finland, Infotech Oulu, the post-doc fellow position of Infotech Oulu, and Tekes Fidipro Program. Moreover, Xiaopeng Hong is partly supported by the Natural Science Foundation of China under the contract No. 61572205. Also, we gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.
© Springer International Publishing AG 2017. This is a post-peer-review, pre-copyedit version of an article published in Computer Vision – ACCV 2016 Workshops : ACCV 2016 International Workshops, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part I. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-319-54407-6_34.