Content Specific Feature Learning for Fine-Grained Plant Classification
نویسندگان
چکیده
We present the plant classification system submitted by the QUT RV team to the LifeCLEF 2015 plant task. Our system learns a content specific feature for various plant parts such as branch, leaf, fruit, flower 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.
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