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The graph-based model has attracted lots of research attention and achieved remarkable progress in this task, which constructs graphs to formulate the intrinsic structure of any image. Nevertheless, the existing graph-based salient object detection methods still have certain limitations and face two major challenges: (1) Previous graphs are constructed by the Gaussian kernel, but they are often corrupted by original noise. (2) They fail to capture common representations and complementary diversity of multi-view features. Both of these degrade saliency performance. In this paper, we propose a novel method, called multi-scale pure graphs with multi-view subspace clustering for salient object detection. Its main contribution is a new, two-stage graph, constructed and constrained by multi-view subspace clustering with sparsity and low rank. One of the advantages is that the multi-scale pure graphs upgrade the saliency performance from the propagation of noise in the graph matrix. Another advantage is that the multi-scale pure graphs exploit consistency and complementary information among multi-view features, which can effectively boost the capability of the graphs. In addition, to verify the impact of the symmetry of the multi-scale pure graphs on the salient object detection performance, we compared the proposed two-stage graphs, which included cases considering the multi-scale pure graphs and those not considering the multi-scale pure graphs. The experimental results were derived using several RGB benchmark datasets and several state-of-the-art algorithms for comparison. The results demonstrate that the proposed method outperforms the state-of-the-art approaches in terms of multiple standard evaluation metrics. This paper reveals that multi-view subspace clustering is beneficial in promoting graph-based saliency detection tasks.<\/jats:p>","DOI":"10.3390\/sym17081262","type":"journal-article","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T08:33:06Z","timestamp":1754555586000},"page":"1262","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Scale Pure Graphs with Multi-View Subspace Clustering for Salient Object Detection"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1006-4115","authenticated-orcid":false,"given":"Mingxian","family":"Wang","sequence":"first","affiliation":[{"name":"School of Earth Science and Engineering, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"},{"name":"Shaanxi Key Laboratory of Petroleum Accumulation Geology, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"}]},{"given":"Hongwei","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Earth Science and Engineering, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"},{"name":"Shaanxi Key Laboratory of Petroleum Accumulation Geology, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3022-6222","authenticated-orcid":false,"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Petroleum Engineering, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"}]},{"given":"Wenjie","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Earth Science and Engineering, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"},{"name":"Shaanxi Key Laboratory of Petroleum Accumulation Geology, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1256-9954","authenticated-orcid":false,"given":"Fan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Science, Xi\u2019an Shiyou University, Xi\u2019an 710065, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hsu, K.J., Lin, Y.Y., and Chuang, Y.Y. 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