Product Image Recognition Based on Deep Learning

In order to satisfy the users＇ emotional demands for products, a method based on deep learning was proposed for product image recognition. The method obtained a product image dataset by five-point semantic difference method. VGGNet, a kind of convolutional neural network, was trained with the dataset to establish product image deep model. The typical product of chair was applied to train and verify the product image deep model, which achieved the accuracy up to 93.33%. Furthermore, to prove the superiority of the method, it was compared with the traditional methods using support vector machine （SVM） and shallower convolutional neural network such as CaffeNet. The result shows that the proposed method achieves the accuracy about 5% better than SVM and about 4%-10% than CaffeNet. In addition, convolved features were visualized for explaining the recog- nition progress, demonstrating the abstract progress from low-level to high-level of feature mapping.