Chaotic-to-Fine Clustering for Unlabeled Plant Disease Images

• URL: http://arxiv.org/abs/2101.06820v1
• Date: Mon, 18 Jan 2021 00:44:12 GMT
• Title: Chaotic-to-Fine Clustering for Unlabeled Plant Disease Images
• Authors: Uno Fang, Jianxin Li, Xuequan Lu, Mumtaz Ali, Longxiang Gao and Yong Xiang
• Abstract summary: Current annotation for plant disease images depends on manual sorting and handcrafted features by agricultural experts. We propose a self-supervised clustering framework for grouping plant disease images based on the vulnerability of Kernel K-means.
• Score: 14.22068250199567
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current annotation for plant disease images depends on manual sorting and handcrafted features by agricultural experts, which is time-consuming and labour-intensive. In this paper, we propose a self-supervised clustering framework for grouping plant disease images based on the vulnerability of Kernel K-means. The main idea is to establish a cross iterative under-clustering algorithm based on Kernel K-means to produce the pseudo-labeled training set and a chaotic cluster to be further classified by a deep learning module. In order to verify the effectiveness of our proposed framework, we conduct extensive experiments on three different plant disease datatsets with five plants and 17 plant diseases. The experimental results show the high superiority of our method to do image-based plant disease classification over balanced and unbalanced datasets by comparing with five state-of-the-art existing works in terms of different metrics.

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