This paper proposed a fast image retrieval method that combines deep hashing technology and VP-Tree indexing to address the precision and speed challenges in high-dimensional feature image retrieval.The method first designed a lightweight deep convolutional encoding network which introduced convolutional block attention modules and spatial pyramid pooling tech-niques to enhance feature extraction capabilities.Then,through this network model,the high-dimensional features of each image in the image dataset were transformed into binary hash codes,which were combined with their corresponding image IDs to form a hash table.Subsequently,a VP-Tree was constructed using the hash codes of all images.During image retrieval,the hash code of the query image was used to quickly find the nearest nodes in the VP-Tree.Finally,the corresponding result images were re-trieved from the hash table based on the hash values of these nodes.Experimental results showed that the proposed method sig-nificantly improved retrieval speed while maintaining high retrieval accuracy(the retrieval speed on MNIST,FASHION-MNIST,and CIFAR-10 was increased by 24.17,8.61,and 4.01 times,respectively).
维普
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