Color image retrieval using taken images
Now-a-days in many applications content based image retrieval from large resources has become an area of wide interest. In this paper we present a color-based image retrieval system that uses color and texture as visual features to describe the content of an image region. To speed up retrieval and similarity computation, the database images are segmented and the extracted regions are clustered according to their feature vectors. This process is performed offline before query processing, therefore to answer a query our system need not search the entire database images; instead just a number of candidate images are required to be searched for image similarity. Our proposed system has the advantage of increasing the retrieval accuracy and decreasing the retrieval time. The experimental evaluation of the system is based on a 1,000 real taken color image database. From the experimental results, it is evident that our system performs significantly better and faster compared with other existing systems. In our analysis, we provide a comparison between retrieval results based on relevancy for the given ten classes. The results demonstrate that each type of feature is effective for a particular type of images according to its semantic contents, and using a combination of them gives better retrieval results for almost all semantic classes.
. C. C. Venters and M. Cooper, Content-based image retrieval, Technical Report, JISC Technology Application Program, 2014.
. M. Myron Flickner, H. Sawhney, W. Niblack, J. Ashley, â€œQuery by image content: The QBIC systemâ€, In IEEE Computer, pp. 23-31, Sept.2015.
. Chad Carson, Serge Belongie, Hayit Greenspan, Jitendra Malik, â€œBlobworld: Image segmentation using Expectation-Maximization and its application to image queryingâ€, Third International Conference on Visual InformationSystems, 2015. In Proceedings of IEEE International Conference on Image Processing, pp.568-571,2015 
. Selvam, S., & S. Thabasu Kannan. " Analysis of the major issues in Cloud Computing Environments." IARS' International Research Journal [Online], 5.2 (2015)
. V. E. Ogle and M. Stonebraker, Chabot: retrieval from object oriented data base of images, IEEE Computer, vol. 28, no. 9, pp. 40-8, Sept. 2015.
. Selvam, S. and S. Thabasu Kannan, â€œImage Retrieval Optimization With Genitic Algorithmâ€, published IJAER, International Journal of Applied Engineering Research as Special issue, Volume No 10, Issue No 55(2015) and IJAER is indexed by SCOPUS, And also listed in Anna University Chennai Annexure II-2014(Sl.No.8565).
. Michael Ortega, Yong Rui, Kaushik Chakrabarti, Sharad Mehrotra and Thomas S. Huang, Supporting Similarity Queries in MARS. In Proceeding of the ACM International Multimedia Conference, pp. 403-413, 2014.
. Tat-Seng Chua, Wai-Chee Low, and Chun-Xin Chu, Relevance feedback techniques for color-based image retrieval, In Proceedings of Multi-Media Modeling'13, IEEE Computer Society, pp 24-31, 2013.
. Selvam, S., & S. Thabasu Kannan. " An Integration of Genetic Algorithm and Projected Clustering for Optimization of Content Based Image Retrieval System." IARS' International Research Journal [Online], 4.2 (2014)
. D. Feng, W. Siu, H. Zhang (Eds.), â€œColor Image Retrieval and Management. Technological Fundamentals and Applications,â€ Multimedia Signal Processing Book, Chapter1, Springer-Verlag, Berlin Heidelberg New York, 2013, page:1-26.
. Selvam S. and S. Thabasu Kannan, â€œA New Technique for Color-Based Image Retrieval System Using Histogramâ€, published IJAER, International Journal of Applied Engineering Research as Special issue, Volume No 10, Issue No 82(2015), ISSN 0973-4562 and IJAER is indexed by SCOPUS.
. J. Smithand S. Chang, â€œVisualseek: A Fully Automated Content-Based Image Query System,â€ Proceedings of the 4th ACM international conference on Multimedia table of contents, Boston, Massachusetts, United States, Nov.2016, pp.87-98.
. J. Caicedo, F. Gonzalez, E. Romero, E. Triana, â€œDesign of an Image Database with Content-Based Retrieval Capabilities, â€In Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology,Santiago,Chile,December17-19,2014.
. R. Zhang, and Z. Zhang, â€œA Clustering Based Approach to Efficient Image Retrieval, â€Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence (ICTAIâ€™14),Washington,DC,Nov.2014,pp.339-346.
. A. Natsev, R. Rastogi, and K. Shim, â€œWALRUS: A Similarity Retrieval Algorithm for Image Databases,â€ IEEE Trans. On Knowledge and Data Engineering, vol.16, pp. 301-318, Mar. 2014.
. Selvam S. and S. Thabasu Kannan, â€œAn Empirical Review on Enhancing the Robustness of Multi resolution Water Markingâ€, published IJAER, International Journal of Applied Engineering Research as Special issue, Volume No 10, Issue No 82(2015), ISSN 0973-4562 and IJAER is indexed by SCOPUS.
. R. Picardand and T. Kabir, â€œFinding Similar Patterns in Large Image Databases,â€ Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, vol.5, pp. 161-164, NewYork2015.
. D. Zhang, â€œImproving Image Retrieval Performance by Using Both Color and Texture Features,â€ In Proc. of IEEE 3rd International Conference on Image and Graphics (ICIG14), Hong Kong, China, Dec.18-20,2014,pp.172-175.
. R. Datta, J. Li, and J. Wang, â€œContent-based image retrieval-approaches and trends of the new age,â€ ACM Computing Surveys, vol.40, no.2, Article5, pp.1-60, April2015.
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