KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Image Clustering using Color, Texture and Shape Features

  • Sleit, Azzam (Computer Science Department, King Abdulla II School for Information Technology) ;
  • Abu Dalhoum, Abdel Llatif (Computer Science Department, King Abdulla II School for Information Technology) ;
  • Qatawneh, Mohammad (Computer Science Department, King Abdulla II School for Information Technology) ;
  • Al-Sharief, Maryam (Computer Science Department, King Abdulla II School for Information Technology) ;
  • Al-Jabaly, Rawa'a (Computer Science Department, King Abdulla II School for Information Technology) ;
  • Karajeh, Ola (Computer Science Department, King Abdulla II School for Information Technology)
  • Received : 2010.10.11
  • Accepted : 2010.12.03
  • Published : 2011.01.31
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Abstract

Content Based Image Retrieval (CBIR) is an approach for retrieving similar images from an image database based on automatically-derived image features. The quality of a retrieval system depends on the features used to describe image content. In this paper, we propose an image clustering system that takes a database of images as input and clusters them using k-means clustering algorithm taking into consideration color, texture and shape features. Experimental results show that the combination of the three features brings about higher values of accuracy and precision.

Keywords

References

  1. Y. Rui, T.S. Huang and S. F. Chang, "Image retrieval: past, present and future," Journal of Visual Communication and Image Representation, pp. 1-23, 1997.
  2. Y. Cai-xiang and Q. Shu-bo, "Image retrieval algorithm based on texture and color feature," WASE International Conference on Information Engineering, vol. 1, pp. 125-128, 2009.
  3. P. Androutsos, A. Kushki, K. N. Plataniotis and A. N. Venetsanopoulos, "Aggregation of color and shape features for hybrid query generation in content based visual information retrieval," Signal Processing, vol. 85, no. 2, pp. 385-393, 2005.
  4. N.S. Vassilieva, "Content-based image retrieval methods," Programming and Computer Software, vol.35, no. 3, pp. 158-180, 2009.
  5. M. Stricker and M. Orengo, "Similarity of color images," in Proc. of the SPIE Conference on Storage and Retrieval for Image and Video Databases III, vol. 2420, pp.381-392, 1995.
  6. Marius Tico, Taneli Hayerinen and Pauli Kuosmanen, "A method of color histogram creation for image retrieval," in Proc. of Nordic Signal Processing Symposium (NORSIG), pp.157-160, 2000.
  7. Khaled Hammouda and Ed Jernigan, "Texture segmentation using gabor filters," Thesis, University of Waterloo, Ontario, Canada, 2008.
  8. Manish Maheshwari, Sanjay Silakari and Mahesh Motwani, "Image clustering using color and texture," in Proc. of the 1st International Conference on Computational Intelligence, Communication Systems and Networks, pp. 403-408, 2009.
  9. Gang Zhang, Z.M. Ma, Qiang Tong, Ying He and Tienan Zhao, "Shape feature extraction using Fourier Descriptors with brightness in content-based medical image retrieval," in Proc. of the International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp.71-74, 2008.
  10. Priti Maheswary and Namita Srivastava, "Retrieval of remote sensing images using colour & texture attribute," International Journal of Computer Science and Information Security, vol. 4, no. 1, pp. 19-23, 2009.
  11. P.S. Hiremath and J. Pujari, "Content based image retrieval using color, texture and shape features," in Proc. of the 15th International Conference on Advanced Computing and Communications (ADCOM 2007), pp.780-784, 2007.
  12. M. Kokare, P.K. Biswas and B.N. Chatterji, "Texture image retrieval using rotated wavelet filters," Pattern Recognition Letters, vol. 28, no. 10, pp. 1240-1249, 2007.
  13. E. Saykol, U. Gudukbay and O. Ulusoy, "A histogram-based approach for object-based query-byshape- and-color in image and video databases," Image and Vision Computing, vol. 23, no. 13, pp. 1170-1180, 2005.
  14. W. Raffe, J.Hu, F. Zambetta, and K. Xi, "A dual-layer clustering scheme for real-time identification of plagiarized massive multiplayer games (MMG) assets," in Proc. of the 5th IEEE Conference on Industrial Electronics and Applications (ICIEA), Taiwan, June 2010.
  15. J. Hu and F. Han, "A pixel-based scrambling scheme for digital medical images protection," Journal of Network and Computer Applications, vol. 32, no. 4, pp. 788-794, 2009.

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