Abstract
In this paper, most of the existing Hashing methods is mapping the hand extracted features to binary code, and designing the loss function with the label of images. However, hand-crafted features and inadequacy considering all the loss of the network will reduce the retrieval accuracy. Supervised hashing method improves the similarity between sample and hash code by training data and labels of image. In this paper, we propose a novel deep hashing method which combines the objective function with pairwise label which is produced by the Hamming distance between the label binary vector of images, quantization error and the loss of hashing code between the balanced value as loss function to train network. The experimental results show that the proposed method is more accurate than most of current restoration methods.
Highlights
Researchers proposed many efficient retrieval technology in the past ten years, the most successful methods including image retrieval method based on tree, retrieval method based on Hashing which’s representative method is a locality sensitive hashing (LSH)[1] and image retrieval method based on vector quantization
Compared with other methods, hashing method have the high efficiency in Hamming distance calculation and the advantages of storage space, so hashing method is very popular in large-scale similar image retrieval
Deep Neural Network Hashing (DNNH)[18] and Deep supervised hashing (DPSH) developed an end-toend model which can update the binary code by the learned image and better display the ability of deep learning
Summary
Researchers proposed many efficient retrieval technology in the past ten years, the most successful methods including image retrieval method based on tree, retrieval method based on Hashing which’s representative method is a locality sensitive hashing (LSH)[1] and image retrieval method based on vector quantization. A hashing method for learning by using multi-labels as supervised information is proposed on the basis of many previous methods. The method is combined with deep learning and hashing method, forming the deep supervised hashing method for multi-label images. According to the Hamming distance between any pairwise images, we get the pairwise labels, and get the label matrix, which simplifies the multiple labels of the image, and is more convenient as supervised information; Design a loss function in this paper which contains three components: the difference between hash and image semantics, quantization error when image features are quantized into hash codes, the balance rate for each bit as 0 or 1; Add the hidden layer with the sigmoid function in the model which makes the input of hashing method is closer to 0 or 1
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