Springer Verlag;Switzerland: Springer International Publishing AG
摘要:
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 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 BoW-based one and state-of-the-art approaches, conducted on the same dataset. 出版者： Switzerland: Springer International Publishing AG 出版日期： 2015 出處： Intelligent Systems in Science and Information 2014, 2015, Vol.591, p.77-91 資源來源： Springer Books 版權： Springer International Publishing Switzerland 2015 識別號： ISSN: 1860-949X 識別號： ISBN: 9783319146539 識別號： ISBN: 331914653X 識別號： EISSN: 1860-9503 識別號： EISBN: 9783319146546 識別號： EISBN: 3319146548 識別號： DOI: 10.1007/978-3-319-14654-6_5 識別號： OCLC: 958503382 識別號： LCCallNum: Q342Q334-342TJ210.2-
