A Survey: Over Various Hashing Techniques Which Provide Nearest Neighbor Search in Large Scale Data

International Journal of Computer Trends and Technology (IJCTT)          
© 2016 by IJCTT Journal
Volume-36 Number-2
Year of Publication : 2016
Authors : Mahendra Kumar Ahirwar, Dr. Jitendra Agrawal


Mahendra Kumar Ahirwar, Dr. Jitendra Agrawal "A Survey: Over Various Hashing Techniques Which Provide Nearest Neighbor Search in Large Scale Data". International Journal of Computer Trends and Technology (IJCTT) V36(2):101-106, June 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
Hashing is most popular technique which provides an efficient and accurate way to nearest neighbor search in large scale data. In large scale image retrieval data is represents in the form of semantic similarity presented in labeled pair of images. Thus unsupervised techniques are efficient to provide solution for these problems, supervised hashing technique is required to provide desired solution. In this paper a survey over these techniques is presented. A Multi-view alignment based hashing technique is presented which uses regularized kernel nonnegative matrix factorization (RKNMF) to enhance the performance of the nearest neighbor search, A composite hashing for multiple information search is presented. There are some other techniques are also presented, which presents an overview over the hashing techniques used for large scale image search.

[1] Li Liu, Mengyang Yu, Ling Shao “Multi-view Alignment Hashing for Efficient Image Search” IEEE, March 2015.
[2] Dan Zhang, Fei Wang, Luo Si “Composite Hashing with Multiple Information Sources” ACM, 2011. [3] Saehoon Kim and Seungjin Choi “MULTI-VIEW ANCHOR GRAPH HASHING” IEEE, 2013.
[4] Ji Wan, Pengcheng Wu, Steven C. H. Hoi, Peilin Zhao, Xingyu Gao, Dayong Wang, Yongdong Zhang, Jintao Li “Online Learning to Rank for Content-Based Image Retrieval” IJCAI, 2015.
[5] Weihao Kong, Wu-Jun Li, Minyi Guo “Manhattan Hashing for Large-Scale Image Retrieval” ACM, 2012.
[6] Jun Wang, Sanjiv Kumar, Shih-Fu Chang “Semi- Supervised Hashing for Scalable Image Retrieval” IEEE, 2012.
[7] Jile Zhou, Guiguang Ding, Yuchen Guo “Latent Semantic Sparse Hashing for Cross-Modal Similarity Search” ACM, 2013.
[8] Y. Kang, S. Kim, and S. Choi, “Deep learning to hash with multiple representations.” in IEEE International Conference on Data Mining, 2012.
[9] D. D. Lee and H. S. Seung, “Learning the parts of objects by nonnegative matrix factorization,” Nature, vol. 401, no. 6755, pp. 788–791, 1999.
[10] Novi Quadrianto, Christoph H. Lampert “Learning Multi-View Neighborhood Preserving Projections” 2011.
[11] S. Z. Li, X. Hou, H. Zhang, and Q. Cheng, “Learning spatially localized, parts-based representation,” in IEEE Conference on Computer Vision and Pattern Recognition, 2001.
[12] P. O. Hoyer, “Non-negative matrix factorization with sparseness constraints,” Journal of Machine Learning Research, vol.5, 2004.
[13] Q. Gu and J. Zhou, “Neighborhood preserving nonnegative matrix factorization.” in British Machine Vision Conference, 2009.
[14] D. Cai, X. He, J. Han, and T. S. Huang, “Graph regularized nonnegative matrix factorization for data representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 8, 2011.
[15] Yi Zhen and Dit-Yan Yeung “Co-Regularized Hashing for Multimodal Data” 2012.
[16] Kristen Grauman and Rob Fergus “Learning Binary Hash Codes for Large-Scale Image Search” 2011.
[17] Gregory Shakhnarovich, Paul Viola, Trevor Darrell “Fast Pose Estimation with Parameter Sensitive Hashing” 2010.
[18] Jonathan Ventura, Tobias H¨ollerer “Fast and Scalable Keypoint Recognition and Image Retrieval using Binary Codes” 2011.
[19] D. Zhang, Z.-H. Zhou, and S. Chen, “Non-negative matrix factorization on kernels,” in Pacific Rim International Conference on ArtificialIntelligence, 2006.
[20] S. An, J.-M. Yun, and S. Choi, “Multiple kernel nonnegative matrix factorization,” in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2011.
[21] L. Van der Maaten and G. Hinton, “Visualizing data using t-SNE,” Journal of Machine Learning Research, vol. 9, no. 11, 2008.
[22] J. C. Bezdek and R. J. Hathaway, “Some notes on alternating optimization,” Advances in Soft Computing-AFSS, pp. 288–300, 2002.
[23] S. P. Boyd and L. Vandenberghe, Convex optimization. Cambridge university press, 2004.
[24] W. Zheng, Y. Qian, and H. Tang, “Dimensionality reduction with category information fusion and nonnegative matrix factorization for text categorization,” Artificial Intelligence and Computational Intelligence, 2011.

Hashing, Nearest Neighbors search, image retrieval, Multi-view Alignment.