Wu Xie,∗ Mengyin Cui,∗ Manyi Liu,∗ Peilei Wang,∗ and Baohua Qiang∗∗
Multi-label, image retrieval, residual network, attention mechanism, deep learning
Multi-label image retrieval is one of the important approaches in the field of image data processing to find similar images from image databases according to a given query example. However, the large amount of data and high-latitude features cause a large amount of calculation, and the extracted image feature vectors contain complex background information, resulting in low accuracy of multi-label image retrieval. To solve this problem, the attention mechanism is introduced to present a novel method of deep hashing multi-label image retrieval in this paper. Firstly, the residual network model is constructed with deep hash using the attention mechanism to identify the approximate positions of multiple objects from multi-label images. Secondly, the loss functions of class cross-entropy are utilized to improve the deep learning models via attention mechanisms. Experimental results show that the proposed method has higher accuracy on two multi-label data sets than other benchmark models, which plays important roles in the image data processing fields.
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