MHI-CNN Model for Fine-grained Classification of Product Image

MHI-CNN Model for Fine-grained Classification of Product Image

Mingxia LinCuihua Li 

School of Information Science and Technology, Xiamen University, Xiamen, P.R.China

School of Business and management, JiMei University, Xiamen, P.R.China

Page: 
123-139
|
DOI: 
https://doi.org/10.18280/ama_b.600108
Received: 
15 March 2017
|
Accepted: 
15 April 2017
|
Published: 
31 March 2017
| Citation

OPEN ACCESS

Abstract: 

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.

Keywords: 

fine-grained classification (FGC), convolutional neural network CNN), deep learning

1. Introduction
2. Related Work
3. Framework of MHI-CNN Model
4. Experiments
5. Conclusion
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