2022
DOI: 10.21203/rs.3.rs-1904975/v1
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GoMIC: Multi-view Image Clustering via Self-supervised Contrastive Heterogeneous Graph Co-learning

Abstract: Graph learning is being increasingly applied to image clustering to reveal intra-class and inter-class relationships in data. However, existing graph learning-based image clustering focuses on grouping images under a single view, which under-utilises the information provided by the data. To address that, we propose a self-supervised multi-view image clustering technique under contrastive heterogeneous graph learning. Our method computes a heterogeneous affinity graph for multi-view image data. It conducts Loca… Show more

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References 46 publications
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