Image-Based Plant Disease Detection: A Comparison of Deep Learning and Classical Machine Learning Algorithms

The goal of person reidenti?cation (Re-ID) is to identify a given pedestrian from a network of non-overlapping surveillance cameras. Existing methods follow the supervised learning paradigm which requires pairwise labeled training data for each pair of cameras. However, this limits their scalability to real-world applications where abundant unlabeled data are available. To address this issue, we propose a multi-feature fusion with adaptive graph learning model for unsupervised ReID. Our model aims to negotiate comprehensive assessment on the consistent graph structure of pedestrians with the help of special information of feature descriptors. Speci?cally, we incorporate a multi-feature dictionary learning and adaptive multifeatured graph learning into a uni?ed learning model such that the learned dictionaries are discriminative and the subsequent graph structure learning is accurate. An alternating optimization algorithm with proved convergence is developed to solve the ?nal optimization objective

Keywords: Machine Learning, Crop Diseases, Deep Learning.