Abstract

The identification of different clothing style attributes is helpful for designers to grasp the clothing style, and it is also helpful for consumers to find clothes with the same style attributes according to their own preferences. This is becoming more important in the clothing design, Internet, and e-commerce industries. It is of great significance to carry out clothing style similarity matching and classification recognition. For the problem of clothing style similarity matching and classification recognition, the traditional algorithm stays in the stage of qualitative analysis and subjective evaluation, unable to quantitatively and objectively determine clothing style, resulting in the decline of clothing style similarity matching effect and classification recognition accuracy. Therefore, a similarity matching, classification, and recognition algorithm of clothing style based on the double-layer model in the context of the Internet of Things is designed. The double-layer model is constructed through the target detection layer and target segmentation layer. The double-layer model is used to realize the segmentation of clothing image. The overall similarity evaluation index of image style is obtained according to the image gradient to complete the similarity matching of clothing style. On this basis, the clothing image features are extracted and input into the support vector machine classifier to complete the clothing classification and recognition. The experimental results show that the proposed garment style similarity matching and classification recognition algorithm based on the two-layer model has high image segmentation accuracy, good style similarity matching effect, high classification recognition accuracy, and high efficiency, which proves that the algorithm is feasible for garment style similarity matching and classification recognition using the two-layer model and can be further applied in the field of garment design.

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