Operational Multi-Modal Distance Metric Learning to Image Reclamation

  • Authors

    • L Lavanya
    • Chebrolu Ujwala Pavani
    • Gadchanda Vineeth
    • Borada Lavanya
    2018-05-31
    https://doi.org/10.14419/ijet.v7i2.32.15725
  • Multi-Modal Distance, Image Reclamation.

  • Distance learning is an eminent technique that improves the search for images based on content. Although widely studied, most DML approaches generally recognize a modalization training framework that teaches a metric distance or a combination of distances in which several types of characteristics are simply interconnected. DML methods of that type suffer some critical limitations (a) Some feature types can significantly overwhelm others with the DML assignment, due to different attributes, and (b) the distance learning standard in the combined metric properties can be consumed using the feature attribute approach combined. In this article we refer to these the restrictions are reviewed online- multimodal distance metric training scheme (OMDML), which explores a dual duplication online learning scheme. (c) learn to optimize the distance metric in each owner space separately; and (d) learn find the optimal combination of different types of characteristics. To overestimate the cost of DML in sophisticated areas, we offer a low level OMDML algorithm that not only reduces estimated costs, but also guarantees high accuracy. We are here carried out exhaustive experiments to estimate the performance of the algorithms proposed for the restoration of multimedia images.

     

     

  • References

    1. [1] M. S. Lew, N. Sebe, C. Djeraba, and R. Jain,“Content-basedmultimedia information retrieval: State of the art and challenges,†ACMTrans.Multimedia Comput., Commun. Appl., vol. 2, no. 1, pp. 1–19, 2006

      [2] Y.Jing and S. Baluja, “Visualrank: Applying pagerank to Large-scale image search,†IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 11, pp. 1877–1890, Nov. 2008.

      [3] D.GrangierandS.Bengio,“A discriminative Kernel-based approach to rank images from text queries,†IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 8, pp. 1371–1384, Aug. 2008.S. C. Hoi, W. Liu, and

      [4] S.-F. Chang, “Semi-supervised distance metric learning for collaborative image retrieval,†in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2008, pp. 1–7.

      [5] J.Sivic, B.C. Russell, A. A. Efros, A. Zisserman, and W. T. Freeman, “Discovering objects and their location in images,†in Proc. IEEE Conf. Compute. Vis. Pattern Recog., 2005, pp. 370–377.

      [6] A. Globerson and S. Roweis, “Metric learning by collapsing classes,†in Proc. Adv. Neural Inf. Process. Syst., 2005, pp. 451–458.

      [7] L. Yang, R. Jin, R. Sukthankar, and Y. Liu, “An efficient algorithm for local distance metric learning,†in Proc. Assoc. Advancement Artif. Intell, 2006, pp. 543–548.

      [8] B. S. Manjunath and W.-Y. Ma, “Texture features for browsing and retrieval of image data,†IEEE Trans. Pattern Anal. Mach. Intell., vol. 18, no. 8, pp. 837–842, Aug. 1996.

      [9] A. W. M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content-based image retrieval at the end of the early years,†IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 12, pp. 1349–1380, Dec. 2000.

      [10] Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Chunyan Miao, and Zhi-Yong Liu, “Online Multi-Modal Distance Metric Learning with Application to Image Retrievalâ€, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 28, NO. 2, FEBRUARY 2016

      [11] Y. Rubner, C.Tomasi, and L. J. Guibas, “The earth movers distance as a metric for image retrieval,†Int. J. Comput. Vis., vol. 40, p. 2000, 2000.

  • Downloads

  • How to Cite

    Lavanya, L., Ujwala Pavani, C., Vineeth, G., & Lavanya, B. (2018). Operational Multi-Modal Distance Metric Learning to Image Reclamation. International Journal of Engineering & Technology, 7(2.32), 405-407. https://doi.org/10.14419/ijet.v7i2.32.15725