Semantic clustering based deduction learning for image recognition and classification. (April 2022)

• Record Type: Journal Article
• Authors: Ma, Wenchi; Tu, Xuemin; Luo, Bo; Wang, Guanghui
• Is Part Of: Pattern recognition. Volume 124(2022)
• Publication Date: 2022-04
• Subjects: Deduction learning -- Clustering prior -- Semantic space -- Smooth semantic clustering; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2021.108440
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: The paper proposes a high-level semantic mapping within semantic space to increase the semantic deduction ability of the deep neural network. Different from multi-level label learning, the proposed model can not only learn features but also deduct high-level semantic expression by itself. The deduction learning is realized by the semantic prior and the proposed random search for opposite semantic to ensure the smoothness of semantic clustering. The proposed model achieves stable convergence and high classification accuracy. It can be taken as a plug-in module for various deep learning applications. Abstract: The paper proposes a semantic clustering based deduction learning by mimicking the learning and thinking process of human brains. Human beings can make judgments based on experience and cognition, and as a result, no one would recognize an unknown animal as a car. Inspired by this observation, we propose to train deep learning models using the clustering prior that can guide the models to learn with the ability of semantic deducing and summarizing from classification attributes, such as a cat belonging to animals while a car pertaining to vehicles. The proposed approach realizes the high-level clustering in the semantic space, enabling the model to deduce the relations among various classes during the learning process. In addition, the paper introduces a semantic prior based random search for the opposite labels to ensure the smooth distribution of the clustering and the robustness of the classifiers. The proposed approach is supported theoretically and empirically through extensive experiments. We compare the performance across state-of-the-art classifiers on popular benchmarks, and the generalization ability is verified by adding noisy labeling to the datasets. Experimental results demonstrate the superiority of the proposed approach.