@inproceedings{adfa845cafcf4ed48a9068928ad4bdf6,
title = "Fine-grained apparel image recognition based on deep learning",
abstract = "There are many styles and details of apparel, including coat length, collar design, sleeve length and other detail properties. The e-commerce platform that manages apparel products needs to quickly and effectively identify the attribute categories of apparel for quick retrieval. Apparel image data contains many detailed features that can be easily deformed and occluded. Traditional image recognition technology has been unable to meet the requirements of its classification accuracy. The neural network based on deep learning can classify the fine-grained attributes of complex objects well after training. In this work, we use the apparel image data to train convolutional neural network for the classification of fine-grained attributes. To improve the classification accuracy, we also integrate the results of different models. The experiments show that the results of multi-model fusion are better than those of single model.",
keywords = "CNN, Deep learning, Fine-grained image recognition",
author = "Jia He and Xi Jia and Junli Li and Shiqi Yu and Linlin Shen",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; Artificial Intelligence on Fashion and Textiles Conference, AIFT 2018 ; Conference date: 27-06-2018 Through 29-06-2018",
year = "2019",
doi = "10.1007/978-3-319-99695-0_21",
language = "English",
isbn = "9783319996943",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "171--178",
editor = "Wong, {Wai Keung}",
booktitle = "Artificial Intelligence on Fashion and Textiles - Proceedings of the Artificial Intelligence on Fashion and Textiles AIFT Conference 2018",
address = "Germany",
}