Hashing via Efficient Addictive Kernel for Logistics Image Classification

Abstract

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.