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Image Retrieval Using a Deep Attention-Based Hash
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DOI：10.1109/ACCESS.2020.3011102.png)
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Abstract
En 中文
Image retrieval is becoming more and more important due to the rapid increase of the number of images on the web. To improve the efficiency of computing the similarity of images, hashing has moved into the focus of research. This paper proposes a Deep Attention-based Hash (DAH) retrieval model, which combines an attention module and a convolutional neural network to obtain hash codes with strong representability. Our DAH has the following features: The Hamming distance between the hash codes generated by similar images is small and the Hamming distance of hash codes of dissimilar images has a larger constant value. The quantitative loss from Euclidean distance to Hamming distance is minimized. DAH has a high image retrieval precision: We thoroughly compare it with ten state-of-the-art approaches on the CIFAR-10 dataset. The results show that the Mean Average Precision (MAP) of DAH reaches more than 92% in terms of 12, 24, 36 and 48 bit hash codes on CIFAR-10, which is better than what the state-of- art methods used for comparison can deliver.
Keywords:
Image retrieval
Hamming distance
Semantics
Computational modeling
Feature extraction
Machine learning
Binary codes
Content-based image retrieval
depth-wise separable convolution kernel
Hamming distance
pairwise loss
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