Abstract
With the increasing development of e-commerce platforms, it has become a growing demand to use collar types to retrieve clothing on shopping websites. However, due to the lack of image training data sets for collar classification, the task of collar image classification faces many challenges such as multiple poses, multiple noises, and small detection areas. There is currently no specific collar image classification research report. The existing image classification algorithms are not ideal in the actual collar classification task. To this end, this paper collected 39248 images of network collars, constructed a small collar image data set with four categories called Collar-Four, and proposed a classification algorithm called SqueezeCNet. The algorithm is improved by adding a CBAM block to the Fire module in SqueezeNet. The improved module is called FireC. In the experimental part, the initialization model and transfer model of SqueezeCNet are compared with the random initialization model and transfer model of traditional convolutional neural networks on the Collar-Four dataset. The initialization model of SqueezeCNet obtains an accuracy of 76.66%, and the classification effect is better than random initialization model and transfer model of traditional convolutional neural networks. The transfer model of SqueezeCNet has an accuracy of 79.74%, which is the best among all experimental models. In addition, an ablation experiment was performed on the classification algorithm proposed in this paper, which further proved the effectiveness of the method in this paper. Experiments show that the application of SqueezeCNet on the Collar-Four dataset is feasible, and this algorithm can effectively solve the real-world collar image classification problem with noisy background.
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Acknowledgment
This work is partially supported by the National Natural Science Foundation of China under Grant Nos. 61962006, 61802035, 61772091; the Project of Science Research and Technology Development in Guangxi under Grant Nos. AA18118047, AD18126015, AB16380272; thanks to the support by the BAGUI Scholar Program of Guangxi Zhuang Autonomous Region of China (2016 [21], 2019 [79]); the National Natural Science Foundation of Guangxi under Grant Nos. 2018GXNSFAA138005.
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Qin, X., Huang, C., Wu, J., Yuan, C. (2020). A Classification Algorithm for Real Collar Images. In: Huang, DS., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12463. Springer, Cham. https://doi.org/10.1007/978-3-030-60799-9_31
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