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Analysis of Multiple Features and Classifier Techniques Combination for Image Pattern Recognition

  • Ashish ShindeEmail author
  • Abhijit Shinde
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 673)

Abstract

Automatic visual pattern recognition is complex and highly researched area of image processing. This research aims to study various pattern recognition algorithms, cloth pattern recognition is presented as research problem and to find out best combination suited for the cloth pattern recognition problem. The dataset is collected from CCNY clothing pattern dataset and contains 150 samples of each category (Patternless, Striped, Plaid, and Irregular). The presented study compares all combinations of three different feature extraction techniques and three classifier techniques. Feature extraction techniques used here are Radon Feature Extraction, projection of rotated gradient, and quantized histogram of gradients. The classifiers used are KNN, neural network, and SVM classifier. The highest recognition rate is achieved using Radon Signature feature and KNN classifier combination which reaches to 93.7% of accuracy.

Keywords

Clothing pattern recognition Texture analysis Image feature extraction Classifier 

References

  1. 1.
    Van Gool L, Dewaele P, Osterlinck A (1983). Texture analysis anno 1983. Computer Vision Graphics Image Processing 29(12):336–57.Google Scholar
  2. 2.
    S. Hidayati. W. Cheng, and K. Hua, “Clothing Genre Classification by Exploiting the Style Elements,” In Proc. ACM Multimedia, 2012.Google Scholar
  3. 3.
    Larry S. Davis, Steven A. Johns, J. K. Aggarwal, “Texture Analysis Using Generalized Co-Occurrence Matrices”, IEEE Transactions on Pattern Analysis and Machine Intelligence (Volume: PAMI-1, Issue: 3, July 1979), pp. 251–259.Google Scholar
  4. 4.
    M. Eisa, A. ElGamal, R. Ghoneim and A. Bahey, “Local Binary Patterns as Texture Descriptors for User Attitude Recognition,” International Journal of Computer Science and Network Security, vol. 10 No. 6, June 2010.Google Scholar
  5. 5.
    Y. Kalantidis, L. Kennedy, L. Li, “Getting the Look: Clothing Recognition and Segmentation for Automatic Product Suggestions in Everyday Photos,” ICMR, ACM 978-1-4503-2033-7/13/04, April, 2013.Google Scholar
  6. 6.
    K. Khouzani and H. Zaden, “Radon Transform Orientation Estimation for Rotation Invariant Texture Analysis,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 27, no. 6, pp. 1004–1008, 2005.Google Scholar
  7. 7.
    Lin-Lin Huanga, Akinobu Shimizua, Yoshihoro Hagiharab, Hidefumi Kobatakea, “Gradient feature extraction for classification-based face detection”, Pattern Recognition Volume 36, Issue 11, November 2003, Pages 2501–2511.Google Scholar
  8. 8.
    S.K. Uma, Srujana B.J., “Feature Extraction for Human Detection using HOG and CS-LBP methods”, International Journal of Computer Applications (0975–8887), 2015, pp. 11–14.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Sinhgad College of EngineeringPuneIndia
  2. 2.Bhima Institute of Management and TechnologyKolhapurIndia

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