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

  IJCTT-book-cover
 
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
  10.14445/22312803/IJCTT-V36P118

MLA

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.

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