Approximately Nearest Neighborhood Image Search Using Unsupervised Hashing via Homogeneous Kernels. (23rd May 2018)

• Record Type: Journal Article
• Authors: Li, Jun-Yi; Li, Jian-Hua
• Other Names: Volchenkov Dimitri Academic Editor.
• Is Part Of: Complexity. Volume 2018(2018)
• Publication Date: 2018-05-23
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2018/9671630
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : We propose an approximation search algorithm that uses additive homogeneous kernel mapping to search for an image approximation based on kernelized locality-sensitive hashing. To address problems related to the unstable search accuracy of an unsupervised image hashing function and degradation of the search-time performance with increases in the number of hashing bits, we propose a method that combines additive explicit homogeneous kernel mapping and image feature histograms to construct a search algorithm based on a locality-sensitive hashing function. Moreover, to address the problem of semantic gaps caused by using image data that lack type information in semantic modeling, we describe an approximation searching algorithm based on the homogeneous kernel mapping of similarities between pairs of images and dissimilar constraint relationships. Our image search experiments confirmed that the proposed algorithm can construct a locality-sensitive hash function more accurately, thereby effectively improving the similarity search performance.