An Efficient Re-rank and Fuzzy based Color & Edge Feature Extraction for CBIR
 |
International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2017 by IJCTT Journal | ||
| Volume-49 Number-1 |
||
| Year of Publication : 2017 | ||
| Authors : Dr. V. Umadevi, M.Suvitha | ||
| DOI : 10.14445/22312803/IJCTT-V49P108 |
Dr. V. Umadevi, M.Suvitha "An Efficient Re-rank and Fuzzy based Color & Edge Feature Extraction for CBIR". International Journal of Computer Trends and Technology (IJCTT) V49(1):44-50, July 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract -
Recently, feature extraction methods are in require today for Content Based Image Retrieval (CBIR) and object recognition applications. In previous decade, large database of image sets has grown quickly and will continue in future. Querying and Retrieval of these images in efficient way is needed in order to access the visual content from huge database set. Content based image retrieval (CBIR) gives the explanation for competent retrieval of image from these huge image databases the new propose system attribute is called “Edge Directivity Descriptor and Colour” and integrates in a texture information and histogram colour with re-ranking feature. CEDD feature extraction development consists of a HSV colour two-stage fuzzy-linking algorithm. This descriptor is apposite for correctly retrieving images even in deformation cases such as bend, smoothing and noise. Imperative feature of the CEDD is the low computational power needed for its extraction, in association to the requirements of the most MPEG-7 descriptors. The researchers are makes using WANG database which consists of 1500 images from 10 different classes. Experimental result explains that the proposed approach execute better in terms of precision compared to other existing methods.
References
[1] J.-H. Su, W.-J. Huang, P. S. Yu, and V. S. Tseng, “Efficient relevance feedback for content-based image retrieval by mining user navigation patterns.” IEEE Trans. Knowl. Data Eng., vol. 23, no. 3, pp. 360–372, 2011.
[2] P. Wu, S. C. H. Hoi, P. Zhao, C. Miao, and Z. Liu, “Online multi-modal distance metric learning with application to image retrieval,” IEEE Trans. Knowl. Data Eng., vol. 28, no. 2, pp. 454–467,2016.
[3] D. Zhang, J. Wang, D. Cai, and J. Lu, “Self-taught hashing for fast similarity search,” in Proc. Int. ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2010, pp. 18–25.
[4] L. Gao, J. Song, F. Zou, D. Zhang, and J. Shao, “Scalable multimedia retrieval by deep learning hashing with relative similarity learning,” in Proc. ACM Int. Conf. Multimedia (MM), 2015, pp. 903– 906.
[5] J. Cheng, C. Leng, P. Li, M. Wang, and H. Lu, “Semi-supervised multi-graph hashing for scalable similarity search,” Comput. Vis. Image Underst., vol. 124, no. 0, pp. 12–21, 2014.
[6] J. Zhou, G. Ding, and Y. Guo, “Latent semantic sparse hashing for cross-modal similarity search,” in Proc. Int. ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2014, pp. 415–424.
[7] X. Zhu, Z. Huang, H. T. Shen, and X. Zhao, “Linear cross-modal hashing for efficient multimedia search,” in Proc. ACM Int. Conf. Multimedia (MM), 2013, pp. 143–152.
[8] Y. Gong, S. Lazebnik, A. Gordo, and F. Perronnin, “Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 12, pp. 2916–2929, 2013.
[9] X. Zhu, Z. Huang, H. Cheng, J. Cui, and H. T. Shen, “Sparse hashing for fast multimedia search.” ACM Trans. Inf. Syst., vol. 31, no. 2, p. 9, 2013.
[10] X. Zhu, X. Li, and S. Zhang, “Block-row sparse multiview multilabel learning for image classification,” IEEE T. Cybernetics, vol. 46, no. 2, pp. 450–461, 2016.
[11] X. Shen, F. Shen, Q.-S. Sun, and Y.-H. Yuan, “Multi-view latent hashing for efficient multimedia search,” in Proc. ACM Int. Conf. Multimedia (MM), 2015, pp. 831–834.
[12] L. Liu, M. Yu, and L. Shao, “Multiview alignment hashing for efficient image search,” IEEE Trans Image Process., vol. 24, no. 3, pp. 956–966, 2015.
[13] B. Xu, J. Bu, C. Chen, C.Wang, D. Cai, and X. He, “Emr: A scalable graph-based ranking model for content-based image retrieval.” IEEE Trans. Knowl. Data Eng., vol. 27, no. 1, pp. 102–114, 2015.
[14] N.Mahendran,” Collaborative Location Based Sleep Scheduling with Load Balancing in Sensor-Cloud “International Journal of Computer Science and Information Security (IJCSIS), ISSN: 1947-5500, volume 14, Special Issue, October 2016, PP: 20-27.
[15] J. Song, Y. Yang, X. Li, Z. Huang, and Y. Yang, “Robust hashing with local models for approximate similarity search,” IEEE T.Cybernetics, vol. 44, no. 7, pp. 1225–1236, 2014.
[16] J. Yu, D. Tao, and M. Wang, “Adaptive hypergraph learning and its application in image classification,” IEEE Trans Image Process.,vol. 21, no. 7, pp. 3262–3272, 2012
[17] T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y.Wu, “An efficient k-means clustering algorithm: Analysis and implementation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 881–892, 2002.
[18] R Gomathi, N Mahendran, “An efficient data packet scheduling schemes in wireless sensor networks”, in Proceeding 2015 IEEE international Conference on Electronics and Communication Systems (ICECS’15), ISBN: 978-1-4799-7225-8, PP:542-547, 2015
[19] S Vanithamani, N Mahendran, “Performance analysis of queue based scheduling schemes in wireless sensor networks”, in proceeding 2014 IEEE international Conference on Electronics and Communication Systems (ICECS’14), ISBN: 978-1-4799-2320-5, PP: 1-6, 2014.
[20] P Kalaiselvi, N Mahendran, “An efficient resource sharing and multicast scheduling for video over wireless networks “,in proceeding 2013 IEEE international Conference on Emerging Trends in Computing, Communication and Nanotechnology (ICECCN’13), ISBN: 978-1-4673-5036-5, PP:378-383, 2013.
Keywords
Content based image retrieval; Re-ranking, Fuzzy Linking Algorithm; Color & Edge Features.