PLSA-based sparse representation for vehicle color classification

Ssu Ying Wang, Jun-Wei Hsieh, Yilin Yan, Li Chih Chen, Duan Yu Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper proposes a novel vehicle color classification method which uses the concept of probabilistic latent semantic analysis (pLSA) to overcome the problem of sparse representation in data classification. Sparse representation is widely used and quite successful in many vision-based applications. However, it needs to calculate the sparse reconstruction cost (SRC) of each sample to find the best candidate. Because an optimization process is involved, it is very inefficient. In addition, it uses only the residual and does not consider the arrangement (or distribution) of combination coefficients of visual codes in classification. Thus, it often fails to classify categories if they are similar. In this paper, the pLSA concept is first introduced into the sparse representation to build a new classifier without using the SRC measure. The weakness of the pLSA scheme is the use of EM algorithm for updating the posteriori probability of latent class. Because it is very time-consuming, a novel weighting voting strategy is introduced to improve the pLSA scheme for recognizing objects in real time. The advantages of this classifier are: the accuracy is much higher than the SRC scheme and the efficiency is real-time in data classification. Vehicle color classification is demonstrated in this paper to prove the superiority of the new classifier.

Original languageEnglish
Title of host publicationAVSS 2015 - 12th IEEE International Conference on Advanced Video and Signal Based Surveillance
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467376327
DOIs
StatePublished - 19 Oct 2015
Event12th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2015 - Karlsruhe, Germany
Duration: 25 Aug 201528 Aug 2015

Publication series

NameAVSS 2015 - 12th IEEE International Conference on Advanced Video and Signal Based Surveillance

Conference

Conference12th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2015
CountryGermany
CityKarlsruhe
Period25/08/1528/08/15

Keywords

  • Algorithm design and analysis
  • Classification algorithms
  • Color
  • Dictionaries
  • Feature extraction
  • Image color analysis
  • Vehicles

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    Wang, S. Y., Hsieh, J-W., Yan, Y., Chen, L. C., & Chen, D. Y. (2015). PLSA-based sparse representation for vehicle color classification. In AVSS 2015 - 12th IEEE International Conference on Advanced Video and Signal Based Surveillance [7301724] (AVSS 2015 - 12th IEEE International Conference on Advanced Video and Signal Based Surveillance). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AVSS.2015.7301724