Add to ORKGWe present the plant classification system submitted by the QUT RV team to the LifeCLEF 2015 plant task. Our system learns a content speciffic feature for various plant parts such as branch, leaf, fruit, ower and stem. These features are learned using a deep convolutional neural network. Experiments on the LifeCLEF 2015 plant dataset show that the proposed method achieves good performance with a score of 0:633 on the test set.</p
Data augmentation plays a crucial role in increasing the number of training images, which often aids...
International audienceTo recognize tree species from pictures of their leaves, one way is to automat...
This paper investigates the application of deep convolutional neural network (CNN) for herbal plant ...
We present the plant classification system submitted by the QUT RV team to the LifeCLEF 2016 plant t...
We present an overview of the QUT plant classification system\ud submitted to LifeCLEF 2014. This sy...
Plant leaf classification involves identifying and categorizing plant species based on leaf characte...
Manual methods to examine leaf for plant classification can be tedious, therefore, automation is des...
The use of machine learning and computer vision methods for recognizing different plants from images...
This paper studies convolutional neural networks (CNN) to learn unsupervised feature representations...
The use of machine learning and computer vision methods for recognizing different plants from images...
Recent progress in machine learning and deep learning has enabled the implementation of plant and cr...
Fine-grained leaf classification has concentrated on the use of traditional shape and statistical fe...
Classification of plants based on a multi-organ approach is very challenging. Although additional da...
This work describes the plant identification system that we submitted to the ExpertLifeCLEF plant id...
The automatic characterization and classification of plant species is an important task for plant ta...
Data augmentation plays a crucial role in increasing the number of training images, which often aids...
International audienceTo recognize tree species from pictures of their leaves, one way is to automat...
This paper investigates the application of deep convolutional neural network (CNN) for herbal plant ...
We present the plant classification system submitted by the QUT RV team to the LifeCLEF 2016 plant t...
We present an overview of the QUT plant classification system\ud submitted to LifeCLEF 2014. This sy...
Plant leaf classification involves identifying and categorizing plant species based on leaf characte...
Manual methods to examine leaf for plant classification can be tedious, therefore, automation is des...
The use of machine learning and computer vision methods for recognizing different plants from images...
This paper studies convolutional neural networks (CNN) to learn unsupervised feature representations...
The use of machine learning and computer vision methods for recognizing different plants from images...
Recent progress in machine learning and deep learning has enabled the implementation of plant and cr...
Fine-grained leaf classification has concentrated on the use of traditional shape and statistical fe...
Classification of plants based on a multi-organ approach is very challenging. Although additional da...
This work describes the plant identification system that we submitted to the ExpertLifeCLEF plant id...
The automatic characterization and classification of plant species is an important task for plant ta...
Data augmentation plays a crucial role in increasing the number of training images, which often aids...
International audienceTo recognize tree species from pictures of their leaves, one way is to automat...
This paper investigates the application of deep convolutional neural network (CNN) for herbal plant ...