Fine-grained classification (FGC) is a current focus of research, but the problem in the application of product image especially in women's clothing is a great challenge. Because women clothing contain more style, color and details than other clothes, in addition constructing a proper training set for these is very difficult. We propose a new model named MHI-CNN which is multi-scale and heterogeneous integration based on the traditional Convolutional Neural Network (CNN) model. We use multi-scale to get more detail feature and heterogeneous integrate three models to achieve higher accuracy. The experiment results show our model improved the performance of women clothing image classification.
fine-grained classification (FGC), convolutional neural network CNN), deep learning
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