Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Call For Paper : Vol 11, Issue 08, August 2024

Call for Paper

Vol 11, Issue 08, August 2024

Indexing

Clustering and Content-Based Retrieval For Image Database

Author : K Prabhu Kiran 1 K Anil 2 Prasad B 3

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Date of Publication :7th March 2016

Abstract: With the proliferation of image data, the need to search and retrieve images efficiently and accurately from a large image database or a collection of image databases has drastically increased. To address such a demand, a unified framework called Markov Model Mediators (MMMs) is proposed in this paper to facilitate conceptual database clustering and to improve the query processing performance by analyzing the ummarized knowledge. The unique characteristics of MMMs are that it provides the capabilities of exploring the affinity relations among the images at the database level and among the databases at the cluster level respectively, using an effective data mining process. At the database level, each database is modeled by an intra-database MMM which enables accurate image retrieval within the database. Then the conceptual database clustering is performed and cluster-level knowledge summarization is conducted to reduce the cost of retrieving images across the databases. This framework has been tested using a set of image databases, which contain various numbers of images with different dimensions and concept categories. The experimental results demonstrate that our framework achieves better retrieval accuracy via inter-cluster retrieval than that of intra-cluster retrieval with minimal extra effort.

Reference :

    1. Carson, C., Belongie, S., Greenspan, H., and Malik, J. Blobworld: Image Segmentation Using ExpectationMaximization and Its Application to Image Querying. IEEE Trans. on Pattern Analysis and Machine Intelligence, 24,8 (2002), 1026–1038.
    2. Chen, Y. and Wang, J. Z. A Region-based Fuzzy Feature Matching Approach to Content-based Image Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 9 (2002), 1252-1267.
    3. Ciaccia, P., Patella, M., and Zezula, P. M-tree: An Efficient Access Method for Similarity Search in Metric Spaces. In Proceedings of the 23rd VLDB conference, 1997, 426-435.
    4. Cooper, M., Foote, J., and Girgensohn, A. Temporal Event Clustering for Digital Photo Collections. In Proceedings of the Eleventh ACM International Conference on Multimedia, 2003, 364-373
    5. Do, M. N. and Vetterli, M. Rotation Invariant Texture Characterization and Retrieval Using Steerable WaveletDomain Hidden Markov Models. IEEE Transactions on Multimedia, 4, 4 (Dec. 2002), 517-527.
    6. Flickner, M., et al. Query By Image and Video Content: The QBIC System. IEEE Computer, 28, 9 (1995), 23-32.
    7. Gupta, A. and Jain, R. Visual Information Retrieval. Communications of the ACM, 40, 5 (1997), 71-79.
    8. Halkidi, M., Batistakis, Y., and Vazirgiannis, M. On Clustering Validation Techniques. Journal of Intelligent Information Systems, 17(2-3), (December 2001), 107-145.
    9. Jing, F., Li, M., Zhang, H. J., and Zhang, B. An Effective Region-based Image Retrieval Framework. In Proceedings of the Tenth ACM international conference on Multimedia,2002, 456-465.
    10. Kim, D.-H. and Chung, C.-W. QCluster: Relevance Feedback Using Adaptive Clustering for Content-based Image Retrieval. In Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, 2003, 599-610
    11. Kim, S. J., Baberjee, J., Kim, W., and Garza, J. F. Clustering a Dag for Cad Databases. IEEE Transactions on Software Engineering, 14, 11 (Nov. 1988), 1684–1699
    12. Kossmann, D. The State of the Art in Distribute Query Processing. ACM Computing Surveys, 32, 4 (Dec. 2000), 422-469.
    13. Lu, G. Techniques and Data Structures for Efficient Multimedia Retrieval Based on Similarity. IEEE Transactions on Multimedia, 4, 3 (2002), 372-384.
    14. Lu, Y., Zhang, H., Liu, W., and Hu, C. Joint Semantics and Feature Based Image Retrieval Using Relevance Feedback. IEEE Transactions on Multimedia, 5, 3 (2003), 339-347.
    15. Mao, J. and Jain, A. K. A Self-organizing Network for Hyperellipsoidal Clustering (hec). IEEE Transactions on Neural Networks, 7, 1 (1996), 16-29
    16. Natsev, A., Rastogi, R., and Shim, K. WALRUS: A Similarity Retrieval Algorithm for Image Databases. IEEE Transactions on Knowledge and Data Engineering, 16, 3 (2004), 301-316.
    17. Rui, Y., Huang, T., Ortega, M., and Mehrotra, S. Relevance Feedback: A Power Tool for Interactive Contentbased Image Retrieval. IEEE Transactions on Circuit and Video Technology, 8, 5 (1998), 644-655.
    18. Safar, M., Shahabi, C. and Sun, X. Image Retrieval by Shape: A Comparative Study. In Proceedings of IEEE International Conference on Multimedia and Expo (ICME’00), 2000, 141-144.
    19. Sakurai, Y., Yoshikawa, M., Uemura, S., and Kojima, H. The A-tree: An Index Structure for High-Dimensional Spaces Using Relative Approximation. In Proceedings of the International Conference on Very Large Data Bases (VLDB), 2000, 516-526
    20. Saux B. L. and Boujemaa N. Image Database Clustering with SVM-based Class Personalization. IS&T/SPIE Conference on Storage and Retrieval Methods and Applications for Multimedia, 2004.
    21. Sclaro, S., LaCascia, M., Sethi, S., and Taycher, L. Unifying Textual and Visual Cues for Content-based Image Retrieval on the World Wide Web. Computer Vision and Image Understanding, 75 (1/2), (1999), 86–98.
    22. Sheikholeslami, G., Chang, W., and Zhang, A. SemQuery: Semantic Clustering and Querying on Heterogeneous Features for Visual Data. IEEE Transactions on Knowledge and Data Engineering, 14, 5 (2002), 988- 1002.
    23. Shyu, M.-L., Chen, S.-C., Chen, M., and Zhang, C. Affinity Relation Discovery in Image Database Clustering and Content-based Retrieval. Accepted for publication (short paper), ACM International Conference on Multimedia, October 10-16, 2004.
    24. Shyu, M.-L., Chen, S.-C., Chen, M., Zhang, C. and Sarinnapakorn, K. Image Database Retrieval Utilizing Affinity Relationships. In Proceedings of the 1st ACM International Workshop on Multimedia Databases, 2003, 78-85
    25. Shyu, M.-L., Chen, S.-C., and Haruechaiyasak, C. Mining User Access Behavior on the WWW. In Proceedings ofIEEE International Conference on Systems, Man, and Cybernetics, 2001, 1717–1722
    26. Shyu, M.-L., Chen, S.-C., Haruechaiyasak, C., Shu, C.- M., and Li, S.-T. Disjoint Web Document Clustering and Management in Electronic Commerce. In Proceedings of the Seventh International Conference on Distributed Multimedia Systems (DMS’2001), 2001, 494-497.
    27. Shyu, M.-L., Chen, S.-C., and Kashyap, R.L. Organizing a Network of Databases Using Probabilistic Reasoning. In Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, 2000, 1990- 1995
    28. Shyu, M.-L., Chen, S.-C., and Kashyap, R. L. A Probabilistic-Based Mechanism for Video Database Management Systems. In Proceedings of IEEE International Conference on Multimedia and Expo (ICME2000), 2000, 467-470
    29. Shyu, M.-L., Chen, S.-C., and Kashyap, R. L. Database Clustering and Data Warehousing. In Proceedings of the 1998 ICS Workshop on Software Engineering and Database Systems, 1998, 30-37.
    30. Shyu, M.-L., Chen, S.-C., and Shu, C.-M. AffinityBased Probabilistic Reasoning and Document Clustering on the WWW. In Proceedings of the 24th IEEE Computer

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