Abstract
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) algorithm 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 improves the accuracy relative to commonly used methods and make the task of object classification and, content-based retrieval more fast and accurate.
Highlights
In Web 2.0 applications era, we are experiencing the growth of information and confronted with the large amounts of user-based content from internet
With the growth of vision data, we focus on two important aspects of problem including nearest neighbor search and similarity metric learning
In order to test our algorithm performance on dataset, we design some experiments on certain visual task such as Caltech-101 [8] and demonstrate that the performance of algorithm in our paper is beyond the traditional locality-sensitive hashing (LSH) approaches on the dataset, as hash functions can be calculated beyond many kernels
Summary
In Web 2.0 applications era, we are experiencing the growth of information and confronted with the large amounts of user-based content from internet. Recognition, and search tasks, we find that they are very common: The Scientific World Journal (A) in the sample feature space, traditionally LSH approaches can only let us get a relatively high collision probability for items nearby. The low-level and high-level of vision samples have great gap, which means that the gap lowlevel features and high-level semantic information exist To solve this problem, we intend to utilize the side additional information for constructing hash table;. In order to test our algorithm performance on dataset, we design some experiments on certain visual task such as Caltech-101 [8] and demonstrate that the performance of algorithm in our paper is beyond the traditional LSH approaches on the dataset, as hash functions can be calculated beyond many kernels. Arbitrary kernel in ANN is suitable in our scheme; we can find that a lot of similarity hashing functions can be accessed in the task of vision search tasks based on content retrieval
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