A fine-grained classification method for fashion women's wear images based on component detection and visual features

Abstract

The invention relates to a fine-grained classification method for fashionable women's wear images based on component detection and visual characteristics, belonging to the field of computer vision andimage application. The invention firstly detects the parts of the body part of the input fashion women's wear image to be classified and the image in training. Secondly, the detected images of fashion women 's wear are extracted respectively, and the HOG, LBP, color histogram and edge operator of fashion women' s wear images are trained to get the feature extracted images. Then, the defined visual feature descriptors are matched with the extracted four underlying features, and multi-class SVM is used to supervise the learning and training of the fine-grained classifier model. Finally, throughthe fine-grained classifier after training, the fashion women 's clothing image undergone the features classifying achieves fine-grained classification, fashion women' s clothing image classificationresults are output. The detection and classification method adopted by the invention has high accuracy.

Description

technical field [0001] The invention relates to a fine-grained classification method for fashion women's clothing images based on component detection and visual features, and belongs to the field of computer vision and image applications. Background technique [0002] Online shopping has been greatly welcomed by people, showing the development trend of popularization, globalization and mobilization, making fashion clothing classification a more and more popular topic, and fashion clothing classification has been widely used in e-commerce and other fields. Therefore, there have been many improved methods for fashion clothing classification, including the most classic bag of words model, fashion clothing classification method based on deep learning, and random forest, SVM (Support Vector Machine, SVM for short), CNN (Convolutional Neural Network, Convolutional Neural Network, CNN for short) and other methods. Most of the known methods are for coarse-grained classification of ...

AI Technical Summary

Problems solved by technology

In terms of feature extraction and classification, the known methods are mostly based on the underlying features such as color and texture to achieve feature extraction, which cannot make good use of local information, and there are certain limitations in the feature extraction of subtle style differences between fashion clothing categories and within categories. characteristics, only coarse-grained classification of fashion clothing can be achieved

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