Name
Abstract | ||
|---|---|---|
Faced with the massive amount of online shopping clothing images, how to recognize the attributes of clothing images quickly and accurately is a challenging task of image classification. In this paper, a new attribute classification method for clothing images is proposed based on the position relevant features acquired on the region of interest in the image. To extract the relevant regional features, one deep neural network model is designed with the pose estimation. Then the final attributes of clothing images are generated by fusing classification features. The performance of classification by fusion feature is compared with the method by single global feature of the image. The experiment results show that it is effective to combine with pose estimation in attribute classification. And the fusion feature shows good performance regardless of the neural network model. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SmartIoT.2019.00072 | 2019 IEEE International Conference on Smart Internet of Things (SmartIoT) |
Keywords | Field | DocType |
clothing-images,pose-estimation,attributes,deep-neural-network | Pattern recognition,Computer science,Computer network,Clothing,Pose,Artificial intelligence,Region of interest,Artificial neural network,Contextual image classification | Conference |
ISBN | Citations | PageRank |
978-1-7281-3489-5 | 0 | 0.34 |
References | Authors | |
3 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ning Lv | 1 | 31 | 11.32 |
| Huimin Yan | 2 | 0 | 0.34 |
| Shuangsi Zhu | 3 | 0 | 0.34 |
| Chen Chen | 4 | 46 | 6.93 |
| Zhenxing Niu | 5 | 119 | 11.45 |
| Jianlong Zhang | 6 | 0 | 0.34 |