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Abstract As an emerging network structure, the capsule network uses vector output instead of scalar output, which can capture the
spatial relationship between image features and improve the limitations of convolutional neural network. This paper firstly trains the
capsule network to achieve image classification, obtains the predictive label of the image, determines the category of the query im-
age, and then uses the feature parameters in the digital capsule layer of the network as the feature vector of the image. The feature
vector is used to find images similar to the query image in the category set of the query image. In this paper, experiments are carried
out on the FASHION-MNIST and CIFAR10 datasets respectively. The experimental results show that the proposed method can bet-
ter extract the features of the images and obtain good image retrieval results.
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Published: 13 August 2020
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