HU invariant moment and support vector machine-based garment style identification method

Abstract

The invention relates to an HU invariant moment and support vector machine-based garment style identification method. The method comprises the steps of preprocessing a garment image to obtain an outer contour of a garment; extracting an HU invariant moment characteristic of the outer contour of the garment; and performing support vector machine (SVM)-based garment style identification. The preprocessing of the garment image refers to a process that the garment image is subjected to segmentation processing, a 8-adjacent connection region with a maximum area is found as a garment region, and internal pore filling is performed on the garment region; the obtaining of the outer contour of the garment refers to a process that the preprocessed garment image is subjected to external edge detection to obtain a contour image of the garment; the extraction of the HU invariant moment characteristic of the outer contour of the garment refers to a process that a 7-order HU invariant moment eigenvector of a contour shape characteristic of the garment is extracted; and the SVM-based garment style identification refers to garment style multi-classification identification performed by adopting an SVM multi-classifier. The method can achieve the identification accuracy of 83%, has a relatively good effect of identifying garment styles with similar contours, has the characteristics of quickness and accuracy, and can be suitable for identification of garment styles 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 HU invariant moments and support vector machines, in particular to a garment contour image obtained by edge detection after image segmentation processing and based on HU invariant moments and SVM approach to clothing style recognition. 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 contour and inner de...

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

Images