@inproceedings{a3a23d3c94894e6d85131df8d8012dc7,
title = "Multi-level semantic representation for flower classification",
abstract = "Fine-grained classification is challenging since sub-categories have little intra-class variances and large intra-class variations. The task of flower classification can be achieved through highlighting the discriminative parts. Most traditional methods trained Convolutional Neural Networks (CNN) to handle the variations of pose, color and rotation, which only utilize single-level semantic information. In this paper, we propose a fine-grained classification approach with multi-level semantic representation. With the complementary strengths of multi-level semantic representation, we attempt to capture the subtle differences between sub-categories. One object-level model and multiple part-level model are trained as a multi-scale classifier. We test our method on the Oxford Flower dataset with 102 categories, and our result achieves the best performance over other state-of-the-art approaches.",
keywords = "CNN, Fine-grained classification, Multi-level representation",
author = "Chuang Lin and Hongxun Yao and Wei Yu and Wenbo Tang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 18th Pacific-Rim Conference on Multimedia, PCM 2017 ; Conference date: 28-09-2017 Through 29-09-2017",
year = "2018",
doi = "10.1007/978-3-319-77380-3\_31",
language = "英语",
isbn = "9783319773797",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "325--335",
editor = "Bing Zeng and Hongliang Li and \{El Saddik\}, Abdulmotaleb and Xiaopeng Fan and Shuqiang Jiang and Qingming Huang",
booktitle = "Advances in Multimedia Information Processing – PCM 2017 - 18th Pacific-Rim Conference on Multimedia, Revised Selected Papers",
address = "德国",
}