Optimization of Canny Algorithm for Recognition of Female Clothing Styles from the Perspective of Features

The accurate and fast recognition of women's clothing styles is conducive to the classification and recommendation of merchants, and it is also convenient for customers to choose from. This paper optimized the Canny algorithm used to extract the contour edge of the female clothing image and used a convolutional neural network (CNN) classifier to identify clothing styles based on the edges extracted by the Canny algorithm. Finally, the Canny algorithm and the CNN classifier were tested in the simulation experiment. The performance of the CNN classifier was compared with that of the template matching and SVM classifiers, the Resnet34-based recognition method, as well as the target detection network and genetic algorithm-back-propagation neural network combined recognition method. The results demonstrated that the optimized Canny algorithm extracted more distinct contour edges. The CNN classifier exhibited the best performance and the fastest recognition for female clothing styles.