Research on the Fine-grained Plant Image Classification

DOI

10.2991/icmmita-16.2016.239How to use a DOI?

Keywords

fine-grained classification; convolutional neural network; SIFT; bag of word

Abstract

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 [1], Bag of Word (BoW) [2]. We evaluate our method on Oxford 102 Flowers dataset [3], 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.).

Copyright

© 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/).

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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  -