一种相似性保持的线性嵌入哈希方法

doi:10.3969/j.issn.1001-2400.2016.01.017

Abstract

Abstract:

In order to implement quick and effective search, save the storage space and improve the poor performance of affinity relationshaps between high dimensional data and its codes in image retrieval, a new linear embedding hashing is proposed by introducing the preserving similarity. First, the whole data set is clustered into several classes, and then the similarity predicted function is used to maintain affinity relationships between high dimensional data and its codes so as to establish the objective function. By minimizing the margin loss function, the optimal embedded matrix can be obtained. Compared with the existing classic hashing algorithm, experimental results show that the performance of the linear embedding hash algorithm is superior to the other binary encoding strategy on precision and recall.

Key words: approximate nearest neighbor search, hashing, similarity predicted function, precision, recall

CLC Number:

TP391

WANG Xiumei;DING Lijie;GAO Xinbo. Linear embedding Hashing method in preserving similarity[J].J4, 2016, 43(1): 94-98.

References

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Linear embedding Hashing method in preserving similarity

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