Hierarchical Learning of Tree Classifiers for Large-Scale Plant Species Identification | IEEE Journals & Magazine | IEEE Xplore

Hierarchical Learning of Tree Classifiers for Large-Scale Plant Species Identification


Abstract:

In this paper, a hierarchical multi-task structural learning algorithm is developed to support large-scale plant species identification, where a visual tree is constructe...Show More

Abstract:

In this paper, a hierarchical multi-task structural learning algorithm is developed to support large-scale plant species identification, where a visual tree is constructed for organizing large numbers of plant species in a coarse-to-fine fashion and determining the inter-related learning tasks automatically. For a given parent node on the visual tree, it contains a set of sibling coarse-grained categories of plant species or sibling fine-grained plant species, and a multi-task structural learning algorithm is developed to train their inter-related classifiers jointly for enhancing their discrimination power. The inter-level relationship constraint, e.g., a plant image must first be assigned to a parent node (high-level non-leaf node) correctly if it can further be assigned to the most relevant child node (low-level non-leaf node or leaf node) on the visual tree, is formally defined and leveraged to learn more discriminative tree classifiers over the visual tree. Our experimental results have demonstrated the effectiveness of our hierarchical multi-task structural learning algorithm on training more discriminative tree classifiers for large-scale plant species identification.
Published in: IEEE Transactions on Image Processing ( Volume: 24, Issue: 11, November 2015)
Page(s): 4172 - 4184
Date of Publication: 16 July 2015

ISSN Information:

PubMed ID: 26353356

Funding Agency:


References

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