Learning-based leaf image recognition frameworks

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


研究成果: 雜誌貢獻期刊論文同行評審

3 引文 斯高帕斯(Scopus)


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 chapter, we propose two learning-based leaf image recognition frameworks for automatic plant identification and conduct a comparative study between them with existing approaches. First, we propose to learn sparse representation for leaf image recognition. In order to model leaf images, we learn an over-complete 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. Second, we also propose a general bag-of-words (BoW) model-based recognition system for leaf images, mainly used for comparison. We experimentally compare the two learning-based approaches and show unique characteristics of our sparse representation- based framework. As a result, efficient leaf recognition can be achieved on public leaf image dataset based on the two proposed methods. We also show that the proposed sparse representation-based framework can outperform our BoWbased one and state-of-the-art approaches, conducted on the same dataset.

頁(從 - 到)77-91
期刊Studies in Computational Intelligence
出版狀態已發佈 - 2015

ASJC Scopus subject areas

  • 人工智慧


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