科研之友
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摘要
In recent years, the deep hashing image retrieval algorithm has become a hot spot in current research. Although the deep hashing algorithm has achieved good results in image retrieval, how to further improve the accuracy of the image retrieval algorithm and reduce the computational complexity of the algorithm, the two basic requirements of the algorithm, need attention in image retrieval. The paper proposes a new Aggregate Deep Fast Supervised Discrete Hashing (ADFSDH) method for highly efficient image retrieval on large-scale datasets. Specifically, in order to improve the algorithm performance, the paper first proposes a new Aggregate Deep Convolutional Neural Network (ADCNN) mode based on VGG16, VGG19 and transfer learning for effective image feature extraction, which contains two different feature extractors in parallel. And then, the paper proposes a new feature fusion method. When our weighted proportion is consistent with the Mean Average Precision results of two different feature extractors, we can obtain the most accurate description of the image. Firstly, in order to save ADCNN required storage space and improve ADCNN image retrieval efficiency, the Fast Supervised Discrete Hashing algorithm after adjusting the parameters is introduced into the ADCNN model. In addition, ADFSDH unifies feature learning and hash coding into the same framework. The proposed method was experimented on three datasets (CIFAR10, MNIST and FD-XJ), and the result shows that it is superior to the current mainstream approaches in image retrieval.
