Content-Based Image Retrieval Using SOM and DWT

DOI: 10.4236/jsea.2015.82007   PDF   HTML   XML   4,271 Downloads   5,878 Views   Citations


Content-Based Image Retrieval (CBIR) from a large database is becoming a necessity for many applications such as medical imaging, Geographic Information Systems (GIS), space search and many others. However, the process of retrieving relevant images is usually preceded by extracting some discriminating features that can best describe the database images. Therefore, the retrieval process is mainly dependent on comparing the captured features which depict the most important characteristics of images instead of comparing the whole images. In this paper, we propose a CBIR method by extracting both color and texture feature vectors using the Discrete Wavelet Transform (DWT) and the Self Organizing Map (SOM) artificial neural networks. At query time texture vectors are compared using a similarity measure which is the Euclidean distance and the most similar image is retrieved. In addition, other relevant images are also retrieved using the neighborhood of the most similar image from the clustered data set via SOM. The proposed method demonstrated promising retrieval results on the Wang Database compared to the existing methods in literature.

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Huneiti, A. and Daoud, M. (2015) Content-Based Image Retrieval Using SOM and DWT. Journal of Software Engineering and Applications, 8, 51-61. doi: 10.4236/jsea.2015.82007.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Jain, R. and Krishna, K. (2012) An Approach for Color Based Image Retrieval. International Journal of Advanced Electronics and Communication Systems, 2, Paper ID: 10891.
[2] Roy, K. and Mukherjee, J. (2013) Image Similarity Measure Using Color Histogram, Color Coherence Vector, and Sobel Method. International Journal of Science and Research (IJSR), 2, 538-543.
[3] Selvarajah, S. and Kodituwakku, S.R. (2011) Analysis and Comparison of Texture Features for Content Based Image Retrieval. International Journal of Latest Trends in Computing, 2, 108-113.
[4] Kodituwakku, S.R. and Selvarajah, S. (2010) Comparison of Color Features for Image Retrieval. Indian Journal of Computer Science and Engineering, 1, 207-211.
[5] Mangijao Singha, M. and Hemachandran, K. (2012) Content-Based Image Retrieval Using Color Moment and Gabor Texture Feature. International Journal of Computer Science Issues (IJCSI), 9, 299-309.
[6] Zhang, L.N., Wang, L.P. and Lin, W.S. (2012) Generalized Biased Discriminant Analysis for Content-Based Image Retrieval. IEEE Transactions on Systems, Man, and Cybernetics Part B, 42, 282-290.
[7] Stricker, M. and Orengo, M. (1995) Similarity of Color Images Survival Data: An Alternative to Change-Point Models. Proceedings of SPIE Conference on Storage and Retrieval for Image and Video Databases III, Vol. 2420, 381-392.
[8] Liu, Y., Zhang, D.S. and Lu, G.J. (2008) Region-Based Image Retrieval with High-Level Semantics Using Decision Tree Learning. Pattern Recognition, 41, 2554-2570.
[9] Singha, M. and Hemachandran, K. (2012) Content Based Image Retrieval Using Color and Texture. Signal and Image Processing: An International Journal (SIPIJ), 3, 39-57.
[10] Kato, T. (1992) Database Architecture for Content-Based Image Retrieval. Proceedings of the SPIE—The International Society for Optical Engineering, 16, 112-113.
[11] Flickner, M., Sawhney, H., Niblack, W., Ashley, J., Huang, Q., Dom, B., Gorkani, M., Hafne, J., Lee, D., Petkovic, D., Steele, D. and Yanker, P. (1995) Query by Image and Video Content: The QBIC System. IEEE Computer, 28, 23-32.
[12] Thirunavuk, S.K., Ahila, R.P., Arivazhagan, S. and Mahalakshmi, C. (2013) Content Based Image Retrieval Based on Dual Tree Discrete Wavelet Transform. International Journal of Research in Computer and Communication Technology, 2, 473-477.
[13] Lahmiri, S. and Boukadoum, M. (2013) Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images. Journal of Medical Engineering, 2013, 1-13.
[14] Bhuravarjula, H.H. and Kumar, V.N.S. (2012) A Novel Content Based Image Retrieval Using Variance Color Moment. International Journal of Computational Engineering Research, 1, 93-99.
[15] Chang, C.H., Xu, P., Xiao, R. and Srikanthan, T. (2005) New Adaptive Color Quantization Method Based on Self-Organizing Maps. IEEE Transactions on Neural Networks, 16, 237-249.
[16] Herodotou, N., Palataniotis, K.N. and Venetsanopoulus, A.N. (1999) A Color Segmentation Scheme for Object-Based Video Coding. Proceeding of the IEEE Symposium on Advances in Digital Filtering and Signal Processing, Victoria, 5-6 June 1998, 25-29.
[17] Rasti, J., Monadjemi, A. and Vafaei, A. (2011) Color Reduction Using a Multi-Stage Kohonen Self-Organizing Map with Redundant Features. Expert Systems with Applications, 38, 13188-13197.
[18] Zhao, M., Bu, J. and Chen, C. (2002) Robust Background Subtraction in HSV Color Space. Proceedings of SPIE: Multimedia Systems and Applications, Boston, 29-30 July 2002, 325-332.
[19] Sural, S., Qian, G. and Pramanik, S. (2002) Segmentation and Histogram Generation Using the HSV Color Space for Image Retrieval. Proceedings of IEEE International Conference on Image Processing, 2, 589-592.
[20] Scheunders, P. (1997) A Comparison of Clustering Algorithms Applied to Color Image Quantization. Pattern Recognition Letters, 18, 1379-1384.
[21] Kohonen, T. (1990) The Self-Organizing Map. Proceedings of the IEEE, 78, 1464-1480.
[22] Pei, S.-C. and Lo, Y.-S. (1998) Color Image Compression and Limited Display Using Self-Organization Kohonen Map. IEEE Transactions on Circuits and Systems for Video Technology, 18, 191-205.
[23] Kangas, J.A., Kohonen, T. and Laaksonen, J.T. (1990) Variants of Self Organizing Maps. IEEE Trans on Neural Networks, 1,93-99.
[24] IEEE (1990) IEEE Standard Glossary of Image Processing and Pattern Recognition Terminology. IEEE Standard, 610.4-1990.
[25] Kavitha, H., Rao, B.P. and Govardhan, A. (2011) Image Retrieval Based on Color and Texture Features of the Image Sub-Blocks. International Journal of Computer Applications, 15, 33-37.
[26] Moghaddam, H.A., Khajoie, T.T., Rouhi, A.H. and Tarzjan, M.S. (2005) Wavelet Correlogram: A New Approach for Image Indexing and Retrieval. Pattern Recognition, 38, 2506-2518.

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