Approximately Nearest Neighborhood Image Search Using Unsupervised Hashing via Homogeneous Kernels

作者：Li, Jun-Yi^*; Li, Jian-Hua

来源：Complexity, 2018, 2018: 9671630.

DOI：10.1155/2018/9671630

• OBJECT RECOGNITION

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 amethod that combines additive explicit homogeneous kernelmapping 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.

• 出版日期2018
• 单位上海交通大学