Comparative study of leaf image recognition with a novel learning-based approach

Jou Ken Hsiao, Li Wei Kang*, Ching Long Chang, Chih Yang Lin

*此作品的通信作者

研究成果: 書貢獻/報告類型會議論文篇章

22 引文 斯高帕斯(Scopus)

摘要

Automatic plant identification via computer vision techniques has been greatly important for a number of professionals, such as environmental protectors, land managers, and foresters. In this paper, we conduct a comparative study on leaf image recognition and propose a novel learning-based leaf image recognition technique via sparse representation (or sparse coding) for automatic plant identification. In our learning-based method, in order to model leaf images, we learn an overcomplete dictionary for sparsely representing the training images of each leaf species. Each dictionary is learned using a set of descriptors extracted from the training images in such a way that each descriptor is represented by linear combination of a small number of dictionary atoms. Moreover, we also implement a general bag-of-words (BoW) model-based recognition system for leaf images, used for comparison. We experimentally compare the two approaches and show unique characteristics of our sparse coding-based framework. As a result, efficient leaf recognition can be achieved on public leaf image dataset based on the two evaluated methods, where the proposed sparse coding-based framework can perform better.

原文英語
主出版物標題Proceedings of 2014 Science and Information Conference, SAI 2014
發行者Institute of Electrical and Electronics Engineers Inc.
頁面389-393
頁數5
ISBN(電子)9780989319317
DOIs
出版狀態已發佈 - 2014 十月 7
對外發佈
事件2014 Science and Information Conference, SAI 2014 - London, 英国
持續時間: 2014 八月 272014 八月 29

出版系列

名字Proceedings of 2014 Science and Information Conference, SAI 2014

其他

其他2014 Science and Information Conference, SAI 2014
國家/地區英国
城市London
期間2014/08/272014/08/29

ASJC Scopus subject areas

  • 資訊系統

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