Video Classification System Based on Similarity Representation Among Sequential Data

KIPS Transactions on Computer and Communication Systems, Vol. 7, No.1, pp.1-8, January 2018
10.3745/KTCCS.2018.7.1.001, Full Text

Abstract

It is not easy to learn simple expressions of moving picture data since it contains noise and a lot of information in addition to time-based information. In this study, we propose a similarity representation method and a deep learning method between sequential data which can express such video data abstractly and simpler. This is to learn and obtain a function that allow them to have maximum information when interpreting the degree of similarity between image data vectors constituting a moving picture. Through the actual data, it is confirmed that the proposed method shows better classification performance than the existing moving image classification methods.


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Cite this paper

[KIPS Transactions Style]
H. Lee and J. Yang, "Video Classification System Based on Similarity Representation Among Sequential Data," KIPS Transactions on Computer and Communication Systems, Vol.7, No.1, pp.1-8, 2018, DOI: 10.3745/KTCCS.2018.7.1.001.

[IEEE Style]
Hosuk Lee and Jihoon Yang, "Video Classification System Based on Similarity Representation Among Sequential Data," KIPS Transactions on Computer and Communication Systems, vol. 7, no. 1, pp. 1-8, 2018. DOI: 10.3745/KTCCS.2018.7.1.001.

[ACM Style]
Lee, H. and Yang, J. 2018. Video Classification System Based on Similarity Representation Among Sequential Data. KIPS Transactions on Computer and Communication Systems, 7, 1, (2018), 1-8. DOI: 10.3745/KTCCS.2018.7.1.001.