Garment style identification method based on Fourier descriptor and support vector machine

Abstract

The invention relates to a garment style identification method based on a Fourier descriptor and a support vector machine. Preprocessing is carried out on a garment image, the external contour of a garment is obtained, then Fourier description is carried out on the external contour of the garment, and garment style identification based on the support vector machine (SVM) is carried out. The preprocessing of the garment image is the segmentation on the garment image, an 8-shaped communication area with the largest area, namely the garment area, is found, and filling internal gaps of the garment area. The external contour of the garment is obtained in such a manner that external edge detection is carried out on the garment image after the preprocessing, and the contour image of the garment is obtained. The Fourier description of the external contour of the garment is carried out in such a manner that standard Fourier descriptor characteristic vectors of shape characteristics of the garment contour are extracted. According to the invention, multi-classification identification of the garment styles is carried out by SVM multiple classifiers. The identification accuracy reaches 95%, the method is rapid and accurate, and the method is applicable to garment style identification in garment images.

Description

technical field [0001] The invention belongs to the technical field of clothing style recognition, and relates to a clothing style recognition method based on a Fourier descriptor and a support vector machine, in particular to a clothing contour image obtained by performing edge detection after image segmentation processing and based on Fourier Approaches to Garment Style Recognition with Descriptors and SVMs. Background technique [0002] With the advent of the era of big data, merchants can use machine vision technology to analyze consumers' dressing styles, which will help merchants capture the consumption trends of various customer groups and formulate targeted product portfolios, marketing plans and business decisions. At the same time, with the popularization of face computer recognition technology, extracting face features and combining clothing style features will improve the accuracy of identity authentication. Clothing styles are composed of changes in the outer c...

AI Technical Summary

Problems solved by technology

Since the similarity comparison method between WFD feature vectors is complex and depends on the complexity of the target object outline, WFD is not suitable for real-time classification of shapes

Although ELM can greatly improve the speed and generalization ability of network learning, it inevitably causes the hidden danger of over-fitting and reduces the classification effect.

At the same time, An recognizes the clothing design plan without the interference of color and texture, so the clothing outline is smoother and the recognition difficulty is slightly lower; its recognition method is not suitable for clothing with color and texture

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