摘要:The main goal of the present research is to classify images of plants to
species with deep learning. We used convolutional neural network architectures for
feature learning and fully connected layers with logsoftmax output for classification.
Pretrained models on ImageNet were used, and transfer learning was applied. In the
current research image sets published in the scope of the PlantCLEF 2015 challenge
were used. The proposed system surpasses the results of all top competitors of the
challenge by 8% and 7% at observation and image levels, respectively. Our
secondary goal was to satisfy the users’ needs in content-based image retrieval to
give relevant hits during species search task. We optimized the length of the returned
lists in order to maximize MAP (Mean Average Precision), which is critical to the
performance of image retrieval. Thus, we achieved more than 50% improvement of
MAP in the test set compared to the baseline.