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Hand-Crafted Feature Extraction and Deep Learning Models for Leaf Image Recognition
Abstract
Plant leaf recognition has been carried out widely using low-level features. Scale invariant feature transform technique has been used to extract the low-level features. Leaves that match based on low-level features but do not do so in semantic perspective cannot be recognized. To address this, global features are extracted and used. Similarly, convolutional neural networks, deep learning networks, and transfer learning-based neural networks have been used for leaf image recognition. Even then there are issues like leaf images in various illuminations, rotations, taken in different angle, and so on. To address such issues, the closeness among low-level features and global features are computed using multiple distance measures, and a leaf recognition framework has been proposed. Two deep network models, namely Densenet and Xception, are used in the experiments. The matched patches are evaluated both quantitatively and qualitatively. Experimental results obtained are promising for the closeness-based leaf recognition framework as well as the Densenet-based leaf recognition.
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