跳至主要内容

Content-Based Image Retrieval Using SOM and DWT

Read  full  paper  at:
http://www.scirp.org/journal/PaperInformation.aspx?PaperID=54060#.VOBZuyzQrzE

Author(s) 

ABSTRACT
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.
 
Cite this paper
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.
 
References
[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.
http://techniche-edu.in/journals/index.php/ijaecs/article/view/36/29
 
[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.
http://ijsr.net/archive/v2i1/IJSRON2013311.pdf
 
[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.
http://dx.doi.org/10.1109/TSMCB.2011.2165335
 
[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.
http://dx.doi.org/10.1016/j.patcog.2007.12.003
 
[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.http://dx.doi.org/10.1155/2013/104684
 
[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.
http://dx.doi.org/10.1109/TNN.2004.836543
 
[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.
http://dx.doi.org/10.1016/j.eswa.2011.04.132
 
[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.http://dx.doi.org/10.1109/ICIP.2002.1040019
 
[20]Scheunders, P. (1997) A Comparison of Clustering Algorithms Applied to Color Image Quantization. Pattern Recognition Letters, 18, 1379-1384. http://dx.doi.org/10.1016/S0167-8655(97)00116-5
 
[21]Kohonen, T. (1990) The Self-Organizing Map. Proceedings of the IEEE, 78, 1464-1480.
http://dx.doi.org/10.1109/5.58325
 
[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.                eww150215lx
标签：

评论

发表评论

此博客中的热门博文

A Comparison of Methods Used to Determine the Oleic/Linoleic Acid Ratio in Cultivated Peanut (Arachis hypogaea L.)

Cultivated peanut ( Arachis hypogaea L.) is an important oil and food crop. It is also a cheap source of protein, a good source of essential vitamins and minerals, and a component of many food products. The fatty acid composition of peanuts has become increasingly important with the realization that oleic acid content significantly affects the development of rancidity. And oil content of peanuts significantly affects flavor and shelf-life. Early generation screening of breeding lines for high oleic acid content greatly increases the efficiency of developing new peanut varieties. The objective of this study was to compare the accuracy of methods used to classify individual peanut seed as high oleic or not high oleic. Three hundred and seventy-four (374) seeds, spanning twenty-three (23) genotypes varying in oil composition (i.e. high oleic (H) or normal/not high oleic (NH) inclusive of all four peanut market-types (runner, Spanish, Valencia and Virginia), were individually tested ...
发表评论
阅读全文

Location Optimization of a Coal Power Plant to Balance Costs against Plant’s Emission Exposure

Fuel and its delivery cost comprise the biggest expense in coal power plant operations. Delivery of electricity from generation to consumers requires investment in power lines and transmission grids. Placing a coal power plant or multiple power plants near dense population centers can lower transmission costs. If a coalmine is nearby, transportation costs can also be reduced. However, emissions from coal plants play a key role in worsening health crises in many countries. And coal upon combustion produces CO 2 , SO 2 , NO x , CO, Metallic and Particle Matter (PM10 & PM2.5). The presence of these chemical compounds in the atmosphere in close vicinity to humans, livestock, and agriculture carries detrimental health consequences. The goal of the research was to develop a methodology to minimize the public’s exposure to harmful emissions from coal power plants while maintaining minimal operational costs related to electric distribution losses and coal logistics. The objective was...
发表评论
阅读全文

Evaluation of the Safety and Efficacy of Continuous Use of a Home-Use High-Frequency Facial Treatment Appliance

At present, many home-use beauty devices are available in the market. In particular, many products developed for facial treatment use light, e.g., a flash lamp or a light-emitting diode (LED). In this study, the safety of 4 weeks’ continuous use of NEWA TM , a high-frequency facial treatment appliance, every alternate day at home was verified, and its efficacy was evaluated in Japanese individuals with healthy skin aged 30 years or older who complained of sagging of the facial skin.  Transepidermal water loss (TEWL), melanin levels, erythema levels, sebum secretion levels, skin color changes and wrinkle improvement in the facial skin were measured before the appliance began to be used (study baseline), at 2 and 4 weeks after it had begun to be used, and at 2 weeks after completion of the 4-week treatment period (6 weeks from the study baseline). In addition, data obtained by subjective evaluation by the subjects themselves on a visual analog scale (VAS) were also analyzed. Fur...
发表评论
阅读全文