Content Based Image Retrieval using Color Histogram and Discrete Cosine Transform

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2019 by IJCTT Journal
Volume-67 Issue-9
Year of Publication : 2019
Authors : Mohammed M. Elsheh, Sumaia A. Eltomi
  10.14445/22312803/IJCTT-V67I9P105

MLA

MLA Style:Mohammed M. Elsheh, Sumaia A. Eltomi  "Content Based Image Retrieval using Color Histogram and Discrete Cosine Transform" International Journal of Engineering Trends and Technology 67.9 (2019):25-31.

APA Style Mohammed M. Elsheh, Sumaia A. Eltomi. Content Based Image Retrieval using Color Histogram and Discrete Cosine Transform International Journal of Engineering Trends and Technology, 67(9),25-31.

Abstract
This paper proposed a color image retrieval approach based on images' content. This approach is based on extracting an efficient combination of low visual features in the image; color and texture. To extract the color feature, color histogram was used, where the RGB color space was converted into HSV color space, then the color histogram of each space was taken. To extract the texture feature, DCT transformation was used, and DC coefficients are taken meanwhile neglecting AC coefficients. The experimental results were analyzed on the basis of three similarity measures, Manhattan Distance , Euclidean Distance and Mean Square Error. MD similarity measure proved its efficiency in retrieval process compared with other similarity measures at both the execution time and retrieval accuracy. The accuracy and efficiency of the system were evaluated using the precision and recall metrics. The results obtained from the proposed approach showed good results when considering precision measure in evaluation process. The precision was increased by (8.3%) rate compared to the best result of previous studies.

Reference
[1] H. H. Wang, D. Mohamad, and N. A. Ismail, "Approaches, challenges and future direction of image retrieval," arXiv preprint arXiv:1006.4568, 2010.
[2] S. Lata and P. P. Singh, "A Review on Content Based Image Retrieval System," International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), ISSN, vol. 2277, 2014.
[3] R. S. Patil and A. J. Agrawal, "Content-based image retrieval systems: a survey," Advances in Computational Sciences and Technology, vol. 10, pp. 2773-2788, 2017.
[4] W. Zhou, H. Li, and Q. Tian, "Recent advance in content-based image retrieval: A literature survey," arXiv preprint arXiv:1706.06064, 2017.
[5] A. J. Afifi and W. M. Ashour, "Image retrieval based on content using color feature," International Scholarly Research Notices, vol. 2012, 2012.
[6] A. S. GOMASHE and R. KEOLE, "A Novel Approach of Color Histogram Based Image Search/Retrieval," International Journal of Computer Science and Mobile Computing, pp. 57-65, 2015.
[7] M. Gupta and A. K. Garg, "Analysis of image compression algorithm using DCT," International Journal of Engineering Research and Applications (IJERA), vol. 2, pp. 515-521, 2012.
[8] D. T. i Hasta, "Fast Discrete Cosine Transform Algorithm Analysis on IJG JPEG Compression Software," Faculty of Industrial Technology, Gunadarma University, Gunadarma University, 2012.
[9] G. Sorwar and A. Abraham, "DCT based texture classification using soft computing approach," arXiv preprint cs/0405013, 2004.
[10] T. Tsai, Y.-P. Huang, and T.-W. Chiang, "Image retrieval based on dominant texture features," in 2006 IEEE International Symposium on Industrial Electronics, 2006, pp. 441-446.
[11] F. Malik and B. Baharudin, "Analysis of distance metrics in content-based image retrieval using statistical quantized histogram texture features in the DCT domain," Journal of king saud university-computer and information sciences, vol. 25, pp. 207-218, 2013.
[12] Y. Mistry, D. Ingole, and M. Ingole, "Content based image retrieval using hybrid features and various distance metric," Journal of Electrical Systems and Information Technology, 2017.
[13] K. Ponnmoli and D. S. Selvamuthukumaran, "Analysis of Face Recognition using Manhattan Distance Algorithm with Image Segmentation," International Journal of Computer Science and Mobile Computing, vol. 3, pp. 18-27, 2014.
[14] F. Memon, M. A. Unar, and S. Memon, "Image quality assessment for performance evaluation of focus measure operators," arXiv preprint arXiv:1604.00546, 2016.
[15] M. H. Abed and D. S. J. Al-Farttoosi, "Content based Image Retrieval based on Histogram," International Journal of Computer Applications, vol. 110, 2015.
[16] P. Kar and L. Kumari, "Feature Based Image retrieval based on Color," International Research Journal of Engineering and Technology vol. 05, 2018.
[17] P. Hemalath, "Image Retrieval by content using DCT and RGB Projection," International Journal of Computer Science & Communication Networks, vol. 3, p. 134, 2013.
[18] C. Wang, X. Zhang, R. Shan, and X. Zhou, "Grading image retrieval based on DCT and DWT compressed domains using low-level features," Journal of Communications, vol. 10, pp. 64-73, 2015.
[19] L. V. Sree and K. Chaitanya, "Color Image Indexing by Exploiting the Simplicity of the EDBTC Method," International Journal of Research, vol. 04, 2017.
[20] A. Nazir, R. Ashraf, T. Hamdani, and N. Ali, "Content based image retrieval system by using HSV color histogram, discrete wavelet transform and edge histogram descriptor," in 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 2018, pp. 1-6.
[21] C.-H. Su, M. H. A. Wahab, and T.-M. Hsieh, "Image Retrieval based on color and texture features," in 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, 2012, pp. 1816-1819.
[22] P. H. Chandankhede, "Soft Computing Based Texture Classification with MATLAB Tool," International Journal of Soft Computing and Engineering, vol. 2, 2012.

Keywords
Image Retrieval, Content-Based Image Retrieval, Color Histogram, Discrete Cosine Transform.