IEEE Trans Image Process - Data-dependent hashing based on p-stable distribution.

Tópicos

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Resumo

The p-stable distribution is traditionally used for data-independent hashing. In this paper, we describe how to perform data-dependent hashing based on p-stable distribution. We commence by formulating the Euclidean distance preserving property in terms of variance estimation. Based on this property, we develop a projection method, which maps the original data to arbitrary dimensional vectors. Each projection vector is a linear combination of multiple random vectors subject to p-stable distribution, in which the weights for the linear combination are learned based on the training data. An orthogonal matrix is then learned data-dependently for minimizing the thresholding error in quantization. Combining the projection method and orthogonal matrix, we develop an unsupervised hashing scheme, which preserves the Euclidean distance. Compared with data-independent hashing methods, our method takes the data distribution into consideration and gives more accurate hashing results with compact hash codes. Different from many data-dependent hashing methods, our method accommodates multiple hash tables and is not restricted by the number of hash functions. To extend our method to a supervised scenario, we incorporate a supervised label propagation scheme into the proposed projection method. This results in a supervised hashing scheme, which preserves semantic similarity of data. Experimental results show that our methods have outperformed several state-of-the-art hashing approaches in both effectiveness and efficiency.

Resumo Limpo

pstabl distribut tradit use dataindepend hash paper describ perform datadepend hash base pstabl distribut commenc formul euclidean distanc preserv properti term varianc estim base properti develop project method map origin data arbitrari dimension vector project vector linear combin multipl random vector subject pstabl distribut weight linear combin learn base train data orthogon matrix learn datadepend minim threshold error quantiz combin project method orthogon matrix develop unsupervis hash scheme preserv euclidean distanc compar dataindepend hash method method take data distribut consider give accur hash result compact hash code differ mani datadepend hash method method accommod multipl hash tabl restrict number hash function extend method supervis scenario incorpor supervis label propag scheme propos project method result supervis hash scheme preserv semant similar data experiment result show method outperform sever stateoftheart hash approach effect effici

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