Earticle

초록

영어

In this paper, fast image search with efficient additive kernels and kernel locality-sensitive hashing has been proposed. As to hold the kernel functions, recent work has probed methods to create locality-sensitive hashing , which guarantee our approach’s linear time, however existing methods still do not solve the problem of locality-sensitive hashing (LSH) and indirectly sacrifice the loss in accuracy of search results in order to allow fast queries. To improve the search accuracy, we show how to apply explicit feature maps into the homogeneous kernels, which help in feature transformation and combine it with kernel locality-sensitive hashing. We prove our method on several large datasets, and illustrate that it improve the accuracy relative to commonly used methods and make the task of object classification, content-based retrieval more fast and accurate.

목차

Abstract
 1. Introduction
 2. Homogeneous Kernel
 3. Homogeneous Kennel Map
 4. Kernelized Locality-Sensitive Hashing
 5. Experimental Result
 6. Conclusions
 Acknowledgment
 References

키워드

저자

• Xiao-jun Liu [ Department of Logistics and Information Management, Zhuhai College of Jilin University, Zhuhai 519041,Guangdong,China ]
• Qiu-ling Li [ Faculty of Business and Administration, University of Macau, Macau 999078, Macau ]
• Bin Zhang [ Department of Logistics and Information Management, Zhuhai College of Jilin University, Zhuhai 519041,Guangdong,China ]
• Jun-yi Li [ Electrical and Computer Engineering Department, National University of Singapore, Singapore 119077, Singapore ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605