JACIII Vol.16 No.5 pp. 631-640
doi: 10.20965/jaciii.2012.p0631


Representing Visual Complexity of Images Using a 3D Feature Space Based on Structure, Noise, and Diversity

Phuc Q. Le, Abdullah M. Iliyasu, Jesus A. Garcia Sanchez,
Fangyan Dong, and Kaoru Hirota

Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

December 10, 2011
April 17, 2012
July 20, 2012
image processing, feature space, clustering algorithm, fuzzy inference system, visual complexity
A 3D feature space is proposed to represent visual complexity of images based on Structure, Noise, and Diversity (SND) features that are extracted from the images. By representing images using the proposed feature space, the human classification of visual complexity of images as being simple, medium, or complex can be implied from the structure of the space. The structure of the SND space as determined by a clustering algorithm and a fuzzy inference system are then used to assign visual complexity labels and values to the images respectively. Experiments on Corel 1000A dataset, Web-crawled, and Caltech 256 object category dataset with 1000, 9907, and 30607 images respectively using MATLAB demonstrate the capability of the 3D feature space to effectively represent the visual complexity. The proposal provides a richer understanding about the visual complexity of images which has applications in evaluations to determine the capacity and feasibility of the images to tolerate image processing tasks such as watermarking and compression.
Cite this article as:
P. Le, A. Iliyasu, J. Sanchez, F. Dong, and K. Hirota, “Representing Visual Complexity of Images Using a 3D Feature Space Based on Structure, Noise, and Diversity,” J. Adv. Comput. Intell. Intell. Inform., Vol.16 No.5, pp. 631-640, 2012.
Data files:
  1. [1] M. Cardici, V. D. Gesu, M. Petrou, and M. E. Tabacchi, “A Fuzzy Approach to the Evaluation of Image Complexity,” Fuzzy Sets and Systems, pp. 1474-1484, Vol.160, 2009.
  2. [2] F. Yaghmaee and M. Jamzad, “Computing Watermark Capacity in Images according to their Quad Tree,” Int. Symposium on Signal Processing and Information Technology, 2005.
  3. [3] M. Jamzad and F. Yaghmaee, “Using Image Complexity according to Achieving Higher Stability in Digital Watermarking,” Int. Conf. ICIRA, 2004.
  4. [4] Q. Liu, A. H. Sung, B. Ribeiro, M.Wei, Z. Chen, and J. Xu, “Image Complexity and Feature Mining for Steganalysis of Least Significant Bit Matching Steganography,” Int. J. of Information Sciences, Vol.178, pp. 21-36, 2008.
  5. [5] I. Mario, M. Chacon, D. Alma, and S. Corral, “Image Complexity Measure: a Human Criterion Free Approach,” Proc. NAFIPS2005, pp. 241-246, 2005.
  6. [6] J. Rigau, M. Feixas, and M. Sbert, “An Information-Theoretic Framework for Image Complexity, Computational Aesthetics in Graphics, Visualization and Images,” pp. 177-184, 2005.
  7. [7] S. Tai and S. Yang, “A Fast Method for Image Noise Estimation using Laplacian Operator and Adaptive Edge Detection,” ISCCSP, 2008.
  8. [8] E. Rosten, R. Porter, and T. Drummond, “Fast and Better: a Machine Learning Approach to Corner Detection,” ISCCSP, 2008.
  9. [9] P. Q. Le, A. M. Iliyasu, A. S. Garcia, F. Dong, and K Hirota, “A Three Dimensional Feature Space for Representing Visual Complexity of Images using their Structure, Noise, and Diversity,” Proc. ISIS 2011, pp. 417-420, 2011.
  10. [10]˜angz/image.vary.tar, Accessed, 2011.
  11. [11] G. Griffin, A. D. Holub, and P. Perona, “The Caltech-256,” Caltech Technical Report.,
    Accessed, 2011.

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

Last updated on Mar. 01, 2024