Research on the Fine-grained Plant Image Classification
- 10.2991/icmmita-16.2016.239How to use a DOI?
- fine-grained classification; convolutional neural network; SIFT; bag of word
The similarity between different subcategories and scarce training data due to the difficulties of Fine-grained recognition. Even in the same subcategories, there can be some differences due to the distinct color and pose of objects. We propose some models for fine-grained plant recognition by taking advantage of deep Convolutional Neural Network (CNN) and traditional feature based methods including SIFT , Bag of Word (BoW) . We evaluate our method on Oxford 102 Flowers dataset , our results show that the CNN method achieves higher accuracy than the traditional feature based methods. Our results demonstrates state-of-the-art performances on the Oxford 102 Flowers with 88.40% (Acc.).
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Zhifeng Hu AU - Yin Zhang AU - Liang Tan PY - 2017/01 DA - 2017/01 TI - Research on the Fine-grained Plant Image Classification BT - Proceedings of the 2016 4th International Conference on Machinery, Materials and Information Technology Applications PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/icmmita-16.2016.239 DO - 10.2991/icmmita-16.2016.239 ID - Hu2017/01 ER -