JACIII Vol.13 No.3 pp. 255-261
doi: 10.20965/jaciii.2009.p0255


Human-Assisted Fuzzy Image Similarity Analysis Based on Information Compression

Gancho Vachkov

Department of Reliability-based Information Systems Engineering, Faculty of Engineering, Kagawa University, Hayashi-cho 2217-20, Takamatsu, Kagawa 761-0396, Japan

January 5, 2009
February 18, 2009
May 20, 2009
similarity analysis, information compression, unsupervised classification, learning algorithms, fuzzy inference
The fuzzy similarity analysis we propose in this paper is used for unsupervised image classification. We introduce a special growing unsupervised learning algorithm for information compression (granulation) of the original “raw data” (the RGB pixels) of an image with a smaller number of neurons (information granules). Two important parameters are extracted from each image, namely the center of gravity (COG) and the model volume of the image, taken as the number of neurons obtained from information compression. These two features are used as inputs for special fuzzy inference for numerically calculating the degree of similarity between a pair of images. The fuzzy inference procedure can be tuned based on a predefined human preference (list of similar images), thus performing human-assisted similarity analysis. The choice of the optimization algorithm and the selection of the optimization criterion are questions open to the user to answer. The proposed computation scheme for similarity analysis is illustrated on a test example of 16 flower images and results are discussed.
Cite this article as:
G. Vachkov, “Human-Assisted Fuzzy Image Similarity Analysis Based on Information Compression,” J. Adv. Comput. Intell. Intell. Inform., Vol.13 No.3, pp. 255-261, 2009.
Data files:
  1. [1] J. C. Bezdek, “Pattern Recognition with Fuzzy Objective Function Algorithms,” New York: Plenum Press, 1981.
  2. [2] Ch. M. Bishop, “Neural Networks for Pattern Recognition,” Oxford University Press, 2003.
  3. [3] W. Pedrycz, “Knowledge-Based Clustering: From Data to Information Granules,” Wiley-Intersciense, p. 316, 2005.
  4. [4] T. Kohonen, “Self-Organizing Maps,” Third Edition, Springer Series in Information Sciences, Springer, Berlin, 2001.
  5. [5] T. Martinetz, S. Berkovich, and K. Schulten, “Neural-Gas Network for Vector Quantization and Its Application to Time-Series Prediction,” IEEE Trans. Neural Networks, Vol.4, No.4, pp. 558-569. 1993.
  6. [6] L. Xu, A. Krzyzak, and A. Oja, “Rival Penalized Competitive Learning for Clustering Analysis, RBF Net and Curve Detection,” IEEE Trans. Neural Networks, Vol.4, No.4, pp. 636-649, 1993.
  7. [7] G. Vachkov, “Classification of Machine Operations Based on Growing Neural Models and Fuzzy Decision,” CD-ROM Proc. of the 21st European Conf. on Modelling and Simulation, ECMS 2007, Prague, Czech Republic, pp. 68-73, June 4-6, 2007.
  8. [8] G. Vachkov and H. Ishihara, “Learning Algorithms for Compression and Evaluation of Information from Large Data Sets,” CD-ROM Proc. of the SICE Annual Conf. 2007, Takamatsu, Japan, pp. 1837-1844, Sep. 17-20. 2007.
  9. [9] G. Vachkov and H. Ishihara, “On-Line Unsupervised Learning for Information Compression and Similarity Analysis of Large Data Sets,” CD-ROM Proc. of the 2007 IEEE Int. Conf. on Mechatronics and Automation, ICMA 2007, Harbin, China, pp. 105-110, August 5-8, 2007.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 10, 2024