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Comparative Study on CBIR based on Color Feature

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International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 78 - Number 16
Year of Publication: 2013
Authors:
Hany Fathy Atlam
Gamal Attiya
Nawal El-fishawy
10.5120/13605-1387

Hany Fathy Atlam, Gamal Attiya and Nawal El-fishawy. Article: Comparative Study on CBIR based on Color Feature. International Journal of Computer Applications 78(16):9-15, September 2013. Full text available. BibTeX

@article{key:article,
	author = {Hany Fathy Atlam and Gamal Attiya and Nawal El-fishawy},
	title = {Article: Comparative Study on CBIR based on Color Feature},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {78},
	number = {16},
	pages = {9-15},
	month = {September},
	note = {Full text available}
}

Abstract

Content Based Image Retrieval (CBIR) system helps users to retrieve relevant images based on their contents. It finds images in large databases by using a unique image feature such as texture, color, intensity or shape of the object inside an image. This paper presents a comparative study between the feature extraction techniques that based on color feature. These techniques include Color Histogram, HSV Color Histogram and Color Histogram Equalization. In this study, the retrieval process is first done by measuring the similarities between the query image and the images within the WANG database using two approaches: Euclidean distance and correlation coefficients. Then, the comparison is carried out by measuring the accuracy, error rate and elapsed time of each technique.

References

  • D. Feng, W. C. Siu, and H. J. Zhang, "Fundamentals of Content-Based Image Retrieval", Multimedia Information Retrieval and Management—Technological Fundamentals and Applications,New York:Springer ,2003.
  • V. N. Gudivada and V. V. Raghavan, "Content based image retrieval systems", IEEE Computer, vol. 28, Sept. 1995.
  • G. Pass and R. Zabih, "Histogram refinement for content-based image retrieval," in 3rd IEEE Workshop on Applications of Computer Vision, pp. 96–102,1996.
  • L. Cinque, S. Levialdi, and A. Pellicano, "Color-based image retrieval using spatial-chromatic histograms," in IEEE Int. Conf. Multimedia Computing and Systems, pp. 969–973,1999.
  • D. Ashok Kumar and J. Esther, "Comparative Study on CBIR based by Color Histogram Gabor and Wavelet Transform", International Journal of Computer Applications, Vol. 17, No. 3, March 2011.
  • N. S. Chang and K. S. Fu, "Image Query by Pictorial Example," IEEE Trans. Software Engineering, 1980.
  • Y. Rui and T. S. Huang, "Image Retrieval: Current Techniques, Promising Directions, and Open issues, Visual Commun, Image Representation", pp. 39–62, 1999.
  • A. W. M. Smeulders, "Content-based image retrieval at the end of the early years", IEEE Trans. Pattern Anal. Mach. Intel, Vol. 22, pp. 1349–1379, 2000.
  • Wan Siti Halimatul Munirah Wan Ahmad and Mohammad Faizal Ahmad Fauzi, "Comparison of Different Feature Extraction Techniques in Content- Based Image Retrieval for CT Brain images", Multimedia Signal Processing, IEEE 10th Workshop on, pp. 503 – 508,2008.
  • Manimala Singha and K. Hemachandran, "Content Based Image Retrieval using Color and Texture", Signal & Image Processing: An International Journal (SIPIJ), Vol. 3, No. 1, February 2012.
  • D. A. Kumar and J. Esther, "Comparative Study on CBIR based by Color Histogram, Gabor and Wavelet Transform", Vol. 17, No. 3, pp. 37-44, March 2011.
  • R. Chakarvarti and X. Meng, "A Study of Color Histogram Based Image Retrieval", Information Technology: New Generations, ITNG '09. Sixth International Conference on, pp. 1323-1328,2009.
  • Youngeun an and Muhammad Riaz and Jongan Park, "CBIR based on adaptive segmentation of HSV color space" 12th International Conference on Computer Modelling and Simulation, 2010.
  • J. Miralles, Tutorial de GIMP. [Online]. Available: http: //sites. google. com/site/tutorialdegimp/011---teoria-del-color-for%macion-y-mezcla-de-colores –rgb y-cmyk.
  • A. Vadivel, S. Sural, and A. Majumdar, "Human color perception in the HSV space and its aplication in histogram generation for image retrieval," in SPIE Procedings seetings, San José CA,United States of America, 2005.
  • P. B Thawari,N. J janwe,"CBIR Based on Color and Texture", International Journal of Information Technology and Knowledge Management,Vol. 4,No. 1,pp. 129-132,june 2011
  • Liwei Wang, Yan Zhang, and Jufu Feng. "On the Euclidean Distance of Images", IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 27, NO. 8, AUGUST 2005.
  • MATLAB—the language of technical computing, http://www. mathworks. com/products/matlab/index. html
  • J. Li, J. Z. Wang, "Automatic linguistic indexing of pictures by a statistical modeling approach", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 9, pp. 1075-1088, 2003.
  • Wang's dataset http://wang. ist. psll. edll/
  • Aman Chadha,Sushmit Mallik and Ravdeep Johar," Comparative Study and Optimization of Feature Extraction Techniques for Content based Image Retrieval", International Journal of Computer Applications Vol. 52– No. 20, August 2012.
  • S. Manimala and K. Hemachandran, "Image Retrieval-Based on Color Histogram and performance Evaluation of similarity Measurement", Assam University Journal of science & Technology, Vol. 8 Number II ,pp. 94-104,