文章基本信息

标题：Deep Learning for Plant Classification and Content-Based Image Retrieval
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作者：Bálint Pál Gyires-Tóth ; Márton Osváth ; Dávid Papp 等
期刊名称：Cybernetics and Information Technologies
印刷版ISSN：1311-9702
电子版ISSN：1314-4081
出版年度：2019
卷号：19
期号：1
页码：88-100
DOI：10.2478/cait-2019-0005
出版社：Bulgarian Academy of Science
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.
关键词：deep learning; convolutional neural networks; Inception V3; MAP; image; retrieval.