Applying metric space and pivot-based indexing on combined features of bio-images for fast execution of composite queries

  • Authors

    • Meenakshi Srivastava Amity University
    • S.K. Singh Amity University
    • S.Q. Abbas Ambalika Institute of Management and Technology
    2018-01-29
    https://doi.org/10.14419/ijet.v7i1.9009
  • Image Retrieval, Metric Space, Protein Structures, AESA, LAESA, Pivot Indexing.

  • Abstract

    Content based recovery of bio images requires index structures, which can retrieve the similar image objects in time proficient way. Conventional Structure/ sequence based recovery of bio-images (for example, protein structures) experiences, tedious online similarity check from huge web based databases. The general approach of image feature representations follows vector based portrayal. In present manuscript, visual highlights of 3D protein structures and their content highlights have been implemented in isolated metric space, rather than vector space which advances the similarity recovery. At long last, the Visual highlights and Content based highlights are consolidated in one metric space, through the component results of highlight and substance metric. Results have demonstrated that pivot based ordering/ indexing on Combined Index Metric can undoubtedly execute composite content construct queries with respect to bio images in time effective way.

  • References

    1. [1] Mussarat Yasmin, Sajjad Mohsin, Muhammad Sharif, Intelligent Image Retrieval Techniques: A Survey ,Journal of Applied research and technology, Vol 14, 2016

      [2] JohanW. H. Tangelder · Remco C. Veltkamp, “A survey of content based 3D shape retrieval methodsâ€, Multimed Tools Appl (2008)

      [3] Paolo Ciaccia, Marco Patella, and Pavel Zezula. A cost model for similarity queries in metric spaces. In Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, June 1-3, 1998, Seattle, Washington, pages 59–68. ACM Press, 1998.https://doi.org/10.1145/275487.275495.

      [4] Jon Louis Bentley. Multidimensional binary search trees used for associative searching. Communications of the ACM, 18(9):509– 517, 1975.https://doi.org/10.1145/361002.361007.

      [5] https://www-roc.inria.fr/gamma/gamma/Logiciels/index.en.html

      [6] Jon Louis Bentley. Multidimensional binary search trees in database applications. IEEE Transactions on Software Engineering, 5(4):333–340, 1979.https://doi.org/10.1109/TSE.1979.234200.

      [7] Antonin Guttman. R-Trees: A dynamic index structure for spatial searching. In Beatrice Yormark, editor, SIGMOD’84, Proceedings of Annual Meeting, Boston, Massachusetts, pages 47–57. ACM Press, 1984.https://doi.org/10.1145/602259.602266.

      [8] Hanan Samet. The quadtree and related hierarchical data structures. ACM Computing Surveys (CSUR), 16(2):187–260, 1984.https://doi.org/10.1145/356924.356930.

      [9] Stefan Berchtold, Daniel A. Keim, and Hans-Peter Kriegel. The X-tree: An index structure for high-dimensional data. In T. M. Vijayaraman, Alejandro P. Buchmann, C. Mohan, and Nandlal L. Sarda, editors, VLDB’96, Proceedings of 22th International Conference on Very Large Data Bases, September 3-6, 1996, Mumbai (Bombay), India, pages 28–39. Morgan Kaufmann, 1996.

      [10] Andreas Henrich. The LSDh-Tree: An access structure for feature vectors. In Proceedings of the Fourteenth International Conference on Data Engineering, February 23-27, 1998, Orlando, Florida, USA, pages 362–369. IEEE Computer Society, 1998.https://doi.org/10.1109/ICDE.1998.655799.

      [11] Kaushik Chakrabarti and Sharad Mehrotra. The Hybrid Tree: An index structure for high dimensional feature spaces. In Proceedings of the 15th International Conference on Data Engineering, 23-26 March 1999, Sydney, Austrialia, pages 440–447. IEEE Computer Society, 1999.

      [12] H. M. Berman, J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov, and P. E.Bourne. The protein data bank. Nucleic Acids Research, 28:235–242, 2000.https://doi.org/10.1093/nar/28.1.235.

      [13] Sungchul Kim, Postech, York Korea, Fast Protein 3D Surface Search, ICUIMC (IMCOM)’ 13, January 17-19, 2013, Kota Kinabalu, Malaysia Copyright 2013 ACM 978-1-4503-1958-4.

      [14] Meenakshi Srivastava, Dr. S.K.Singh, Dr. S.Q.Abbas, “A Novel Model for Fast And Robust Retrieval of 3D Bio-Images Using Intelligent Vision Algorithmâ€, International Journal of Control Theory and Applications, 9(41) 2016, pp. 617-627.

      [15] Srivastava M., Singh S.K., Abbas S.Q., Neelabh (2018) AMIPRO: A Content-Based Search Engine for Fast and Efficient Retrieval of 3D Protein Structures., Smart Innovation, Systems and Technologies, Springer, Volume 79,https://doi.org/10.1007/978-981-10-5828-8_70.

      [16] Marla Luisa Mic6, Jos6 Oncina, A new version of the Nearest - Neighbour Approximating and Eliminating Search Algorithm (AESA) with linear preprocessing time and memory requirements, Pattern Recognition Letter, 1994, revised 2011.

      [17] Motofumi T. Suzuki, Texture Image Classification using Extended 2D HLAC Features,KEER2014, LINKÖPING | JUNE 11-132014 International Conference On Kansai Engineering And Emotion Research Texture

      [18] Nobuyuki OTSU and Takio Kurita, A New Scheme for Practical Flexible And Intelligent Vision Systems, lAPR Workshop on CV -Special Hardware and Industrial Applications OCT.12-14, 1988, Tokyo.

      [19] A. Herr´aez. Biomolecules in the computer: Jmol to the rescue. Biochemistry and Molecular Biology Education, 34(4):255–261, 2006.https://doi.org/10.1002/bmb.2006.494034042644.

  • Downloads

  • How to Cite

    Srivastava, M., Singh, S., & Abbas, S. (2018). Applying metric space and pivot-based indexing on combined features of bio-images for fast execution of composite queries. International Journal of Engineering & Technology, 7(1), 110-114. https://doi.org/10.14419/ijet.v7i1.9009

    Received date: 2018-01-03

    Accepted date: 2018-01-19

    Published date: 2018-01-29