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Distributed Retrieval of Images using Particle Swarm Optimization and Hadoop
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 71 - Number 8 |
| Year of Publication: 2013 |
| Authors: Darsana B, G. Jagajothi |
10.5120/12375-8719
|
Darsana B, G. Jagajothi . Distributed Retrieval of Images using Particle Swarm Optimization and Hadoop. International Journal of Computer Applications. 71, 8 ( June 2013), 1-5. DOI=10.5120/12375-8719
Abstract
Content based Image Retrieval (CBIR) is the problem of searching for digital images in large databases. It is the vital application of computer vision techniques to the image retrieval problem. One inherent problem associated with Content based Image Retrieval is the response time of the system to retrieve relevant result from the image database. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers. The parallel processing of Hadoop can be leveraged to efficiently retrieve images with very less response time. The proposed approach also avoids the semantic gap in image retrieval by utilizing automatic relevance feedback and meta-heuristic optimization. Automatic relevance feedback is implemented using Latent Semantic Analysis, and Particle swarm optimization provides meta-heuristic based development. The goal of proposed approach is to – cluster relevant images using meta-heuristics in less amount of time effectively.
References
- Song Yan, "Research of Image Retrieval Based on color and texture feature", Computer application and soft computing, 2007, 9(2),pp. 42-50.
- Y. Rui, T. S. Huang, and S. Mehrotra, "Relevance feedback a powerful tool in interactive content-based image retrieval", IEEE Transactions on Circuits and Systems for Video Technology, vol. 8, no. 5, pp. 644-655, 1998.
- H. -J. Zhang, "Relevance feedback in content-based image search", Conference on New Information Technology NIT'01. Beijing, China, May 2001.
- W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Pektovic, P. Yanker, C. Faloutsos, and G Taubin, "The QBIC project: Querying images by content using color, texture and shape," Proceedings of SPIE Storage and Retrieval for Image and Video Databases, San Jose, CA, 1994.
- J. R. Smith and S. -F. Chang, "VisualSEEk: A Fully Automated Content-Based Image Query System," Proceedings of the Fourth ACM International Conference on Multimedia '96, Boston, MA, 1996.
- J. Z. Wang, J. Li, and G. Wiederhold, "SIMPLIcity: Semantics- Sensitive Integrated Matching for Picture LIbraries," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, pp. 947-963, 2001.
- C. Carson, S. Belongie, H. Greenspan, and J. Malik, "Blobworld: Image segmentation using expectation-maximization and its application to image querying," IEEE Transaction on Pattern Analysis and Machine Intelligence.
- Gupta. A, "The Virage image search engine: an open framework for image management", Proceedings in Storage and Retrieval for Image and Video Databases, Proc SPIE 2670, pp 76-87, 1996
- Sutcliffe. A, "Empirical studies in multimedia information retrieval" in Journal of Intelligent Multimedia Information Retrieval (Maybury, M T, ed). AAAI Press, Menlo Park, CA, 1997.
- M. Koskela, J. Laaksonen, and E. Oja, "Use of image subsets in image retrieval with self-organizing maps," in Proc. Int. Conf. Image and Video Retrieval (CIVR), 2004, pp. 508–516.
- G. Bordogna and G. Pasi, "A user-adaptive neural network supporting a rule-based relevance feedback," Fuzzy Sets System, vol. 82, no. 9, pp. 201–211, 1996.
- J. Kennedy and R. C. Eberhart, "Particle swarm optimization," in Proc. IEEE Conf. Neural Networks IV, Piscataway, NJ, 1995.
- Kaushik, R. T. ; Bhandarkar, M. ; Nahrstedt, K. , "Evaluation and analysis of Green HDFS: A self-adaptive, energy-conserving variant of the Hadoop distributed file systems", IEEE Second International Conference on Communication, Networking and Broadcasting, 2010.
- Gonde, Anil Balaji, R. P. Maheshwari, and R. Balasubramanian. "Content-Based Image Retrieval using colour feature and colour bit planes", International Journal of Signal and Imaging Systems Engineering 3. 2 (2010): 105-115.
- Kennedy, Lyndon, Malcolm Slaney, and Kilian Weinberger. "Reliable tags using image similarity: mining specificity and expertise from large-scale multimedia databases. " Proceedings of the 1st workshop on Web-scale multimedia corpus, ACM, 2009.
- Yang, Zhuo, Sei-ichiro Kamata, and Alireza Ahrary. "NIR: Content based image retrieval on cloud computing. ", IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009.
- K. Chandramouli, "Particle swarm optimization and self-organizing maps based image classifier," in Proc. 2nd Int. Workshop Semantic Media Adaptation and Personalization, 2007, pp. 225–228.
- S. C. Hoi, M. R. Lyu, and R. Jin, "A unified log-based relevance feedback scheme for image retrieval," IEEE Transaction on Knowledge and Data Engineering, vol. 18, no. 4, pp. 509–524, Apr. 2006.
- Jia Li, James Z. Wang, ``Automatic linguistic indexing of pictures by a statistical modeling approach,'' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1075-1088, 2003.
- Zhou Bing; Yang Xin-xin, "A content-based parallel image retrieval system," International Conference on Computer Design and Applications (ICCDA), 2010 , vol. 1, no. , pp. V1-332,V1-336, 25-27 June 2010
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