Abstract
In order to meet the needs of diversified image information retrieval in the real world, to solve the “semantic gap” problem of image and natural language conversion, and to optimize the accuracy and efficiency of multi-label classification method, this paper proposes a deep learning and multi-label BR algorithm. An image classification method uses a residual neural network with better overall performance to extract image depth learning features, and takes the extraction result as an input, and generates a result vector through spatial regularization of the image space and the label, and the result is obtained. The elements are added as the final prediction result by directly using the residual network prediction result. The whole network is trained by the softmax loss function. Compared with other traditional models, the spatial relationship results of the labels provide a good regularization effect for multi-label image classification, which improves the accuracy on the NUS-WIDE dataset and its recall rate, etc.
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Funding
This research was funded by the National Natural Science Foundation of China under Grant No. 61762079, and No. 61662070, Innovation ability improvement project of colleges and universities in Gansu Province in 2019, Grant No: 2019B-024, the Fundamental Research Funds for the Central University of Northwest Minzu University, Grant No: 31920180050.
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Wang, X., Xu, J., Hua, J., Hao, Z. (2020). Multi-label Image Classification Optimization Model Based on Deep Learning. In: Hao, Z., Dang, X., Chen, H., Li, F. (eds) Wireless Sensor Networks. CWSN 2020. Communications in Computer and Information Science, vol 1321. Springer, Singapore. https://doi.org/10.1007/978-981-33-4214-9_20
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DOI: https://doi.org/10.1007/978-981-33-4214-9_20
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