kNN Hashing With Factorized Neighborhood Representation

Kun Ding, Chunlei Huo, Bin Fan, Chunhong Pan; The IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1098-1106

Abstract


Hashing is very effective for many tasks in reducing the processing time and in compressing massive databases. Although lots of approaches have been developed to learn data-dependent hash functions in recent years, how to learn hash functions to yield good performance with acceptable computational and memory cost is still a challenging problem. Based on the observation that retrieval precision is highly related to the kNN classification accuracy, this paper proposes a novel kNN-based supervised hashing method, which learns hash functions by directly maximizing the kNN accuracy of the Hamming-embedded training data. To make it scalable well to large problem, we propose a factorized neighborhood representation to parsimoniously model the neighborhood relationships inherent in training data. Considering that real-world data are often linearly inseparable, we further kernelize this basic model to improve its performance. As a result, the proposed method is able to learn accurate hashing functions with tolerable computation and storage cost. Experiments on four benchmarks demonstrate that our method outperforms the state-of-the-arts.

Related Material


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[bibtex]
@InProceedings{Ding_2015_ICCV,
author = {Ding, Kun and Huo, Chunlei and Fan, Bin and Pan, Chunhong},
title = {kNN Hashing With Factorized Neighborhood Representation},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}