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In this paper, we propose a novel approach that is based on multiscale segmentation and fuzzy broad learning. The core idea of our method is to segment the image into different scales, and then the extracted features are fed to the fuzzy broad learning system (FBLS) for training. More specifically, it first segments the image into superpixel blocks at different scales based on the simple linear iterative clustering algorithm. Then, it uses the local binary pattern algorithm to extract texture features and computes the average color information for each superpixel of these segmentation images. These extracted features are then fed to the FBLS to obtain multiscale saliency maps. After that, it fuses these saliency maps into an initial saliency map and uses the label propagation algorithm to further optimize it, obtaining the final saliency map. We have conducted experiments based on several benchmark datasets. The results show that our solution can outperform several existing algorithms. Particularly, our method is significantly faster than most of deep learning-based saliency detection algorithms, in terms of training and inferring time.<\/jats:p>","DOI":"10.1093\/comjnl\/bxaa158","type":"journal-article","created":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T04:11:09Z","timestamp":1606363869000},"page":"1006-1019","source":"Crossref","is-referenced-by-count":5,"title":["Salient Object Detection Based on Multiscale Segmentation and Fuzzy Broad Learning"],"prefix":"10.1093","volume":"65","author":[{"given":"Xiao","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Normal University, Shanghai 201418, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi-Jie","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing 400044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lizhuang","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renjie","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Normal University, Shanghai 201418, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mei-E","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,12,21]]},"reference":[{"key":"2022041811431827500_ref1","first-page":"1007","article-title":"Saliency-based discriminant tracking","volume-title":"IEEE Conf. 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