Clothing Image Classification Algorithm Based on Feature Fusion and Attention
A clothing image classification algorithm based on feature fusion and attention mechanism has been proposed to address the prob-lems of low richness of feature information,weak feature representation ability,and low classification accuracy in clothing image classifica-tion.The algorithm uses the ResNet50 convolutional neural network as the basic classification network structure,enriches the feature informa-tion extracted by the model by fusing features extracted from multiple stages of convolutional layers,and embeds channel and position atten-tion modules in the model to enhance feature representation.Experimental results show that the proposed algorithm achieves an accuracy of 79.69%and 82.22%on self-built datasets and DeepFashion datasets,respectively,which are 1.95%and 1.76%higher than the baseline mod-el.This verifies that the proposed algorithm can extract richer clothing feature information,has stronger feature representation ability,and thus improves the effect of clothing image classification.
万方数据
维普
