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With the growth of image on the web, research on hashing which enableshigh-speed image retrieval has been actively studied. In recent years, varioushashing methods based on deep neural networks have been proposed and achievedhigher precision than the other hashing methods. In these methods, multiplelosses for hash codes and the parameters of neural networks are defined. Theygenerate hash codes that minimize the weighted sum of the losses. Therefore, anexpert has to tune the weights for the losses heuristically, and theprobabilistic optimality of the loss function cannot be explained. In order togenerate explainable hash codes without weight tuning, we theoretically derivea single loss function with no hyperparameters for the hash code from theprobability distribution of the images. By generating hash codes that minimizethis loss function, highly accurate image retrieval with probabilisticoptimality is performed. We evaluate the performance of hashing using MNIST,CIFAR-10, SVHN and show that the proposed method outperforms thestate-of-the-art hashing methods.

PDH : Probabilistic deep hashing based on MAP estimation of Hamming distance