Abstract— The need for automatic feature extraction and comparison has become one of the most important research topics in image based retrieval. That due to the rapid increases in images databases and the difficulties of indexing images based on textual description. CBIR (content based image retrieval) utilizes automatic feature extraction based on color, texture, and shape using image analysis and processing techniques. In this paper a novel two-leveled CBIR system is proposed. In the first level, spatial domain is considered with color features extraction and comparison. While, in the second level a hybrid feature based approach is deployed. In which edge detection and enhancement, morphological operator (Dilate), and 2D-DWT (wavelet transform) are used. WANG database of 1000 images form 10 categories is used to test the proposed system. Moreover, extensive comparisons with the most related systems are conducted and the result are better than the other compared systems.
Keywords-CBIR; Edge Detection; Color Moments; Morphological Dilate and 2D-DWT.
Citation: Jehad Q. Odeh Alnihoud, “Image Retrieval Model Based on Color and Shape Features Utilizing 2D Wavelet Transform”, The World of Computer Science and Information Technology Journal (WSCIT). 2017 Volume 7, Issue 4, pp. 20.25.
 Tian, Q., Sebe, N., Lew, M. S., Loupias, E., and Huang, T. S., “Content-based image retrieval using wavelet-based salient points”. In Proceedings of SPIE – The International Society for Optical Engineering, 4315, pp. 425-436, 2001. DOI: 10.1117/12.410953.
 Rafael C. Gonzalez, and Richard E. Woods, Digital Image Processing, 2nd edition, Beijing: Publishing House of Electronics Industry, 2007.
 Mamta Juneja, Parvinder Singh Sandhu, “Performance evaluation of edge detection techniques for images in spatial domain”, International Journal of Computer Theory and Engineering (IJCTE), vol. 1, Issue 5, pp. 614-621, 2009. DOI: 10.7763/IJCTE.2009.V1.100.
 Gaurav Kumar Srivastava, Rohit Verma, Ruchika Mahrishi, Siddavatam Rajesh, “A novel wavelet edge detection algorithm for noisy images”, International conference on Ultra-Modern Telecommunications & Workshops (ICUMT), pp: 1-8. 2009. DOI: 10.1109/ICUMT.2009.5345404.
 Serra, Jean-Paul, “Image analysis and mathematical morphology”, London; New York: Academic Press, 1982.
 Hill, P., Achim, A. and Bull, D., “The undecimated dual tree complex wavelet transform and its application to bivariate image denoising using a Cauchy model”. In proceedings of. 19th IEEE International Conference on Image Processing (ICIP), pp. 1205-1208, 2012, http://dx.doi.org/10.1109/icip.2012.6467082
 Kalra, M. and Ghosh, D., “Image Compression Using Wavelet Based Compressed Sensing and Vector Quantization”, In proceedings of IEEE 11th International Conference on Signal Processing (ICSP), vol. 1, pp. 640-645, 2012.
 Balamurugan, V. and Anandha Kumar, P., “An integrated color and texture feature based framework for content based image retrieval using 2D wavelet transform”, In proceedings of IEEE International Conference on Computing, Communication and Networking, pp. 1-16, 2008. http://dx.doi.org/10.1109/icccnet.2008.4787734
 Quellec, G., Lamard, M., Cazuguel, G., Cochener, B. and Roux, C., “Fast wavelet-based image characterization for highly adaptive image retrieval”, IEEE Transactions on Image Processing, vol. 21, issue no. 4, pp. 1613-1623, 2012.
 Agarwal, S., Verma, A.K. and Singh, P., “Content based image retrieval using discrete wavelet transform and edge histogram descriptor”, In proceedings of International Conference on Information Systems and Computer Networks (ISCON), pp. 19- 23, 2013. http://dx.doi.org/10.1109/iciscon.2013.6524166
 Wang, Y. and Zhang, W., “Coherence vector based on wavelet coefficients for image retrieval”, In proceedings of IEEE International Conference on Computer Science and Automation Engineering (CSAE), 2, pp. 765-768, 2012. http://dx.doi.org/10.1109/CSAE.2012.6272878
 D. H. Ballard and C. M. Brown, Computer vision, Prentice-Hall, 1982.
 Masrour Dowlatabadi and Jalil Shirazi, “Improvements in edge detection based on mathematical morphology and wavelet transform using fuzzy rules”, International Journal of Electrical and Computer Engineering, vol. 5, issue no.10, pp. 1314-1319, 2011.
 Jiu-Ling Zhao, Jiu-Fen Zhao, De-Xin Ren and Su Yan, “A wavelet based algorithm for edge extraction of images”, In proceedings of International Conference on Machine learning and Cybernetics, pp 3803-3806, 2006.
 Scott, E. U., Computer vision and image processing. International Edition, Prentice-Hall, Inc, 1998.
 Yogita Mistry, D. T. Ingole, M. D. Ingole, “Content based image retrieval using hybrid features and various distance metric”, In press, Journal of Electrical Systems and Information Technology, 2017. http://dx.doi.org/10.1016/j.jesit.2016.12.009
 Lin, Ch.-H., Chen, R.-T., and Chan, Y.-K., “A smart content based image retrieval system based on color and texture features”. Image and Vision Computing, vol. 27, pp. 658-665, 2009.
 Manjusha S., and Newlin Raj, “Content based image retrieval using wavelet transform and feedback algorithm”, International Journal of Innovative Research in Science, Engineering and Technology, vol. 3, Special Issue 5, July 2014.
 Giveki, D., Soltanshahi, A., Shiri, F. and Tarrah, H., “A new content based image retrieval model based on wavelet transform”, Journal of Computer and Communications, vol. 3, pp. 66-73, 2015. http://dx.doi.org/10.4236/jcc.2015.33012.
 Dileshwar Patel, and Amit Yerpude, “Content based image retrieval using color edge detection and Haar wavelet transform”, International Research Journal of Engineering and Technology (IRJET), vol. 2, issue no. 9, pp. 1906-1911, 2015.