Texture Feature Extraction and Classification

  • B. Verma
  • S. Kulkarni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2124)


This paper describes a novel technique for texture feature extraction and classification. The proposed feature extraction technique uses an Auto-Associative Neural Network (AANN) and the classification technique uses a Multi-Layer Perceptron (MLP) with a single hidden layer. The two approaches such as AANN-MLP and statistical-MLP were investigated. The performance of the proposed techniques was evaluated on large benchmark database of texture patterns. The results are very promising compared to other techniques. Some of the experimental results are presented in this paper.


pattern recognition feature extraction neural networks 


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  1. 1.
    Manjunath, B., Ma, W.: Texture Features for Browsing and Retrieval of Image Data. IEEE Transaction on Pattern Analysis and Machine Intelligence, 8 (1996) 837–842CrossRefGoogle Scholar
  2. 2.
    Niblack, W.: The QBIC Project: Querying Images by Content using Color, Texture and Shape. SPIE Proceedings of Storage and Retrieval for Color and Image Video Databases, (1993) 173–187Google Scholar
  3. 3.
    Jain, A., Farrokhnia, F.: Unsupervised Texture Segmentation Using Gabor Filters. Journal of Pattern Recognition, 24 (1991) 1167–1186CrossRefGoogle Scholar
  4. 4.
    Rubner, Y., Tomasi, C: Texture Matrices. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, San-Diego, USA (1998) 4601–4607Google Scholar
  5. 5.
    Lui, F., Picard, R.: Periodicity, directionality and Randomness: Wold Features for Image Modelling and Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18 (1996) 722–733CrossRefGoogle Scholar
  6. 6.
    Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover Publications, New York (1996)Google Scholar
  7. 7.
    Ma, W., Manjunath, B.: Texture Features and Learning Similarity. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, San Francisco, USA (1996)Google Scholar
  8. 8.
    Jones, D., Jackway, P.: Using Granold for Texture Classification. In: Fifth International Conference on Digital Image Computing, Techniques and Applications Perth, Australia (1999) 270–274Google Scholar
  9. 9.
    Wang, L., Liu, J.: Texture Classification using Multiresolution Markov Random Field Models. Journal of Pattern Recognition Letters, 20 (1999) 171–182CrossRefGoogle Scholar
  10. 10.
    Lerner, B., Guterman, H., Aladjem, M., Dinstein, H.: A Comparative Study of Neural Network based Feature Extraction Paradigms. Journal of Pattern Recognition Letters, 20 (1999) 7–14zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • B. Verma
    • 1
  • S. Kulkarni
    • 1
  1. 1.School of Information TechnologyGriffith UniversityAustralia

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