Image retrieval techniques: a survey

  • Authors

    • Aman Dureja
    • Payal Pahwa
    https://doi.org/10.14419/ijet.v7i1.2.9231

    Received date: January 21, 2018

    Accepted date: January 21, 2018

    Published date: December 28, 2017

  • CBIR, Visual Features, Distance Metric Learning (DML), Deep CNN, Hash Function.

  • Abstract

    In the recent years, the development in computer technologies and multimedia applications has led to the production of huge digital images and large image databases, and it is increasing rapidly. There are several different areas in which image retrieval plays a crucial role like Medical systems, Forensic Labs, Tourism Promotion, etc. Thus retrieval of similar images is a challenge. To tackle this rapid growth in digital repositories it is necessary to develop image retrieval systems, which can operate on large databases. There are basically three techniques, which is useful for efficient retrieval of images. With these techniques, the number of methods has been modified for the efficient image retrieval of images. In this paper, we presented the survey of different techniques that has been used starting from Image retrieval using visual features and latest by the deep learning with CNN that contains the number of layers and now becomes the best base method for retrieval of images from the large databases. In the last section, we have made the analysis between various developed techniques and showed the advantages and disadvantages of various techniques.

  • References

    1. M. Norouzi, D. J. Armada, and R. Salakhutdinov. Hamming separa-tion metric learning. In NIPS, pages 1070– 1078, 2012.
    2. G. Chechik, V. Sharma, U. Shalit, and S. Bengio. Vast scale web based learning of picture similitude through positioning. Diary of Machine Learning Research, 11:1109– 1135, 2010.
    3. C. Domeniconi, J. Peng, and D. Gunopulos. Locally versatile metric closest neighbor order. IEEE Trans. Example Anal. Mach. Intell., 24(9):1281– 1285, 2002. https://doi.org/10.1109/TPAMI.2002.1033219.
    4. Krizhevsky, Alex. "ImageNet Classification with Deep Convolu-tional Neural Networks". NIPS'12 Proceedings of the 25th Interna-tional Conference on Neural Information Processing Systems, Pages 1097-1105, December 03 - 06, 2012.
    5. Y. Bengio, A. C. Courville, and P. Vincent. Unsupervised element learning and profound taking in: An audit and new points of view. CoRR, abs/1206.5538, 2012.
    6. K. Q. Weinberger, J. Blitzer, and L. K. Saul. Separation metric learning for expansive edge closest neighbor arrangement. In NIPS, 2005.
    7. G. E. Hinton, S. Osindero, and Y. W. Teh. A quick learning calcula-tion for profound conviction nets. Neural Computation, 18(7):1527– 1554, 2006. https://doi.org/10.1162/neco.2006.18.7.1527.
    8. G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.- r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, et al. Profound neural systems for acoustic displaying in discourse acknowledg-ment: The common perspectives of four research gatherings. Flag Processing Magazine, IEEE, 29(6):82– 97, 2012. https://doi.org/10.1109/MSP.2012.2205597.
    9. R. Salakhutdinov and G. E. Hinton. Profound Boltzmann machines. In AISTATS, pages 448– 455, 2009.
    10. K. Jain and A. Vailaya. Picture recovery utilizing shading and shape. Example Recognition, 29(8):1233– 1244, 1996. https://doi.org/10.1016/0031-3203(95)00160-3.
    11. Yong Rui and Thomas S. Huang. Picture Retrieval: Current Tech-niques, Promising Directions, and Open Issues. Diary of Visual Communication and Image Representation Volume 10, Issue 1, 1 March 1999, Pages 39-62 https://doi.org/10.1006/jvci.1999.0413.
    12. Ying Liu, Dengsheng Zhanga, Guojun Lua,Wei-Ying Ma. An over-view of substance based picture recovery with abnormal state se-mantics. Example Recognition 40 (1) 262 – 282, Elsevier, Jan 2007.
    13. J. - E. Lee, R. Jin, and A. K. Jain. Rank-based separation metric tak-ing in: An application to picture recovery. In CVPR, 2008.
    14. Ji Wan1, Dayong Wang, Steven C.H. Hoi, Pengcheng Wu, Jianke Zhu, Yongdong Zhang, Jintao Li. Profound Learning for Content-Based Image Retrieval: A Comprehensive Study. ACM worldwide gathering on Multimedia, Pages 157-166, 2014.
    15. Huafeng Wang, Yehe Cai, Yanxiang Zhang, Haixia Pan, Weifeng Lv and Hao Han. Profound Learning for Image Retrieval: What Works and What Doesn't. IEEE fifteenth International Conference on Data Mining Workshops, November 14 - 17, 2015.
    16. Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. Pic-ture Super-Resolution Using Deep Convolutional Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence (Vol-ume: 38, Issue: 2), Feb. 1 2016.
    17. Siying Zhu, Bong-Nam Kang and Daijin Kim. A Deep Neural Net-work Based Hashing for Efficient Image Retrieval. 2016 IEEE In-ternational Conference on Systems, Man, and Cybernetics, October 9-12, 2016, Budapest, Hungary.
    18. Domonkos Varga and Tamas Sziranyi. Quick substance based pic-ture recovery utilizing Convolutional Neural Network and hash work. 2016 IEEE International Conference on Systems, Man, and Cybernetics, October 9-12, 2016, Budapest, Hungary.
    19. Hongyang Li, Peng Su, Zhizhen Chiy and Jingjing Wangz. Picture Retrieval and Classification on Deep Convolutional SparkNet. 2016 IEEE International Conference on Signal Processing, Communica-tions and Computing (ICSPCC), 5-8 Aug. 2016.
    20. Collobert, Ronan; Weston, Jason (2008-01-01). "A Unified Archi-tecture for Natural Language Processing: Deep Neural Networks with Multitask Learning". Procedures of the 25th International Conference on Machine Learning. ICML '08. New York, NY, USA: ACM: 160– 167.
    21. "Convolutional Neural Networks (LeNet) – Deep Learning 0.1 doc-umentation". Profound Learning 0.1. LISA Lab. Recovered 31 Au-gust 2013.
    22. "CS231n Convolutional Neural Networks for Visual Recognition". cs231n.github.io.
    23. Le Callet, Patrick; Christian Viard-Gaudin; Dominique Barba (2006). "A Convolutional Neural Network Approach for Objective Video Quality Assessment" (PDF). IEEE Transactions on Neural Networks. 17 (5): 1316– 1327. https://doi.org/10.1109/TNN.2006.879766.
    24. Ciresan, Dan; Ueli Meier; Jonathan Masci; Luca M. Gambardella; Jurgen Schmidhuber (2011). "Adaptable, High Performance Convo-lutional Neural Networks for Image Classification”. Procedures of the Twenty-Second universal joint gathering on Artificial Intelli-gence Volume Two. 2: 1237– 1242, 17 November 2013.
    25. Ciresan, Dan; Meier, Ueli; Schmidhuber, Jürgen (June 2012). "Mul-ti-section profound neural systems for picture order". 2012 IEEE Conference on Computer Vision and Pattern Recognition. New York, NY: Institute of Electrical and Electronics Engineers (IEEE): 3642– 3649. https://doi.org/10.1109/CVPR.2012.6248110.
    26. M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoi-ca, Spark: Cluster registering withworking sets, Cloud Computing, 2010.
    27. S. Ioffe and C. Szegedy, "Bunch standardization: Accelerating pro-found system preparing by decreasing inner covariate move," ICML, 2015.

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  • How to Cite

    Dureja, A., & Pahwa, P. (2017). Image retrieval techniques: a survey. International Journal of Engineering and Technology, 7(1.2), 215-219. https://doi.org/10.14419/ijet.v7i1.2.9231