A Garment Style Recognition Method Based on Fourier Descriptor and Support Vector Machine

Abstract

The invention relates to a clothing style recognition method based on a Fourier descriptor and a support vector machine. Through the preprocessing of the clothing image, the outer contour of the clothing is obtained, and then the Fourier description of the outer contour of the clothing is performed, and then based on the support Garment Style Recognition with Vector Machines (SVM). The preprocessing of the clothing image refers to the clothing image segmentation process, finding the 8-connected region with the largest area is the clothing area, and filling the inner cavity of the clothing area; the acquisition of the outer contour of the clothing refers to the preprocessing of the clothing image. After processing, perform external edge detection to obtain the outline image of the clothing. The Fourier description of the outer contour of the clothing refers to extracting the standardized Fourier descriptor feature vector of the shape feature of the clothing contour. The SVM-based clothing style recognition uses SVM multi-classifiers to perform multi-category recognition of clothing styles. The invention can achieve a recognition accuracy rate of 95%, has the characteristics of fastness and accuracy, and is applicable to the recognition of clothing styles in clothing 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 Fourier descriptors and support vector machines, and in particular to a clothing contour image obtained by edge detection after image segmentation processing and based on Fourier Descriptor and SVM clothing style identification method. Background technique [0002] With the advent of the era of big data, merchants use machine vision technology to analyze consumer dress 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 facial computer recognition technology, extracting facial features and combining with clothing style features will improve the accuracy of identity authentication. The clothing style is composed of changes in the outer contours and inner det...

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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