CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
kNN Hashing with Factorized Neighborhood Representation
Kun Ding1; Chunlei Huo1
Conference NameICCV
Source PublicationICCV
Conference DateDecember 13-16
Conference PlaceSantiago, Chile
AbstractHashing 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.
KeywordFactorized Neighborhood Representation
Indexed ByEI
Document Type会议论文
Corresponding AuthorKun Ding
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Kun Ding,Chunlei Huo. kNN Hashing with Factorized Neighborhood Representation[C],2015.
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