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International Conference on Multi-disciplinary Trends in Artificial Intelligence

MIWAI 2018: Multi-disciplinary Trends in Artificial Intelligence pp 240–246Cite as

Enhanced Texture Representation for Moving Targets Classification Using Co-occurrence

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11248))

Abstract

This paper presents a moving targets identification system in more effective computational cost by using Gray Level Co-occurrence Matrix (GLCM) instead of using the other texture descriptors: the conventional LBP histograms and LBPs with co-occurrence matrix. The aim of this work is to develop an enhanced texture analysis based method for the detection and classification of the moving targets in real environment. Firstly, the system distinguished the moving regions from the background regions by using an Adaptive Gaussian Mixture Model (GMM). The gray level (grayscale intensity or Tone) texture features on a co-occurrence matrix will be extracted from each segmented moving block by the four texture features, energy, homogeneity, correlation and contrast in four directions (0°, 45°, 90°, and 135°) and quantized into a feature vector. These exploited texture features will be used to classify the moving objects using the Support Vector Machine (SVM) classification learner. The walking-dog-14-0-3 test sequence from UCF11 dataset is used in experimentation to show the effectiveness of the proposed feature method.

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Author information

Authors and Affiliations

  1. University of Computer Studies, Yangon, Myanmar

    Chit Kyin Htoo & Myint Myint Sein

Authors
  1. Chit Kyin Htoo
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  2. Myint Myint Sein
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Corresponding author

Correspondence to Chit Kyin Htoo .

Editor information

Editors and Affiliations

  1. Mahasarakham University, Maha Sarakham, Thailand

    Manasawee Kaenampornpan

  2. Center for Computing Technologies, University of Bremen, Bremen, Germany

    Rainer Malaka

  3. Vietnam Academy of Science and Technology, Hanoi, Vietnam

    Duc Dung Nguyen

  4. AIST Tokyo, Tokyo, Japan

    Nicolas Schwind

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Htoo, C.K., Sein, M.M. (2018). Enhanced Texture Representation for Moving Targets Classification Using Co-occurrence. In: Kaenampornpan, M., Malaka, R., Nguyen, D., Schwind, N. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2018. Lecture Notes in Computer Science(), vol 11248. Springer, Cham. https://doi.org/10.1007/978-3-030-03014-8_22

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  • DOI: https://doi.org/10.1007/978-3-030-03014-8_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03013-1

  • Online ISBN: 978-3-030-03014-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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