关键词: K-Means聚类, 服装图像检索, 卷积神经网络, 特征融合

Abstract: The booming of clothing e-commerce has accumulated a large amount of clothing image data,and the “image search” of clothing images has become a hot research direction.Apparel images have rich overall semantic information and a large amount of detailed information,and achieving accurate retrieval is a challenging problem.Traditional me-thods of clothing image based on artificial semantic annotation and methods of image retrieval based on artificially designed content features such as color and texture have significant limitations.This paper proposed a clothing image retrieval method based on multi-layer feature fusion and K-Means clustering of convolutional neural networks,which makes full use of the effectiveness and hierarchy of deep convolutional neural network in image feature extraction,fuses the detailed information and abstract semantic information of different convolutional hierarchical featuresto improve retrieval accuracy,and uses K-Means to improve the retrieval speed.The proposed method firstly performs uniform size processing on the clothing image data set,then uses the convolutional neural network for training and feature extraction,extracts multi-level features of the clothing image from low to high,and then fuses various levels of features.Finally,the K-Means clustering method is used to efficiently retrieve large-scale image data.The experimental results on the DeepFashion sub-category data set Category and Attribute Prediction Benchmark and In-shop Clothes Retrieval Benchmark show that the proposed method can effectively enhance the feature expression ability of clothing images,and improve its retrieval accuracy and retrieval speed.The proposed method is supprior to other mainstream methods.

Key words: Clothing image retrieval, Convolution neural network, Feature fusion, K-Means clustering