{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:28:17Z","timestamp":1760232497866,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T00:00:00Z","timestamp":1668211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Plan-natural Science Foundation Project of Gansu","award":["20JR5RA518","NWNU-LKZD2021-06"],"award-info":[{"award-number":["20JR5RA518","NWNU-LKZD2021-06"]}]},{"name":"Cultivation Plan of Major Scientific Research Projects of Northwest Normal University","award":["20JR5RA518","NWNU-LKZD2021-06"],"award-info":[{"award-number":["20JR5RA518","NWNU-LKZD2021-06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>For the co-saliency detection algorithm of an RGBD image that may have incomplete detection of common salient regions and unclear boundaries, we proposed an improved co-saliency detection method of RGBD images based on superpixels and hypergraphs. First, we optimized the depth map based on edge consistency, and introduced the optimized depth map into the SLIC algorithm to obtain the better superpixel segmentation results of RGBD images. Second, the color features, optimized depth features and global spatial features of superpixels were extracted to construct a weighted hypergraph model to generate saliency maps. Finally, we constructed a weighted hypergraph model for co-saliency detection based on the relationship of color features, global spatial features, optimized depth features and saliency features among images. In addition, in order to verify the impact of the symmetry of the optimized depth information on the co-saliency detection results, we compared the proposed method with two types of models, which included considering depth information and not considering depth information. The experimental results on Cosal150 and Coseg183 datasets showed that our improved algorithm had the advantages of suppressing the background and detecting the integrity of the common salient region, and outperformed other algorithms on the metrics of P-R curve, F-measure and MAE.<\/jats:p>","DOI":"10.3390\/sym14112393","type":"journal-article","created":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T02:34:58Z","timestamp":1668393298000},"page":"2393","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Co-Saliency Detection of RGBD Image Based on Superpixel and Hypergraph"],"prefix":"10.3390","volume":"14","author":[{"given":"Weiyi","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenxia","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengyu","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,12]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Nie, G.Y., Cheng, M.M., Liu, Y., Liang, Z., Fan, D.P., Liu, Y., and Wang, Y. (2019, January 15\u201320). Multi-level context ultra-aggregation for stereo matching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","key":"ref_1","DOI":"10.1109\/CVPR.2019.00340"},{"unstructured":"Zeng, Y., Zhuge, Y., Lu, H., and Zhang, L. (2019, January 27\u201328). Joint learning of saliency detection and weakly supervised semantic segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea.","key":"ref_2"},{"doi-asserted-by":"crossref","unstructured":"Fan, D.P., Ji, G.P., Zhou, T., Chen, G., Fu, H., Shen, J., and Shao, L. (2020). Pranet: Parallel reverse attention network for polyp segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","key":"ref_3","DOI":"10.1007\/978-3-030-59725-2_26"},{"doi-asserted-by":"crossref","unstructured":"Fan, D.P., Wang, W., Cheng, M.M., and Shen, J. (2019, January 15\u201320). Shifting more attention to video salient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","key":"ref_4","DOI":"10.1109\/CVPR.2019.00875"},{"doi-asserted-by":"crossref","unstructured":"Song, H., Wang, W., Zhao, S., Shen, J., and Lam, K.M. (2018, January 8\u201314). Pyramid dilated deeper convlstm for video salient object detection. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","key":"ref_5","DOI":"10.1007\/978-3-030-01252-6_44"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1098","DOI":"10.1109\/TMM.2016.2547343","article-title":"Saliency-guided quality assessment of screen content images","volume":"18","author":"Gu","year":"2016","journal-title":"IEEE Trans. Multimed."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1023\/B:VISI.0000022288.19776.77","article-title":"Efficient graph-based image segmentation","volume":"59","author":"Felzenszwalb","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/34.868688","article-title":"Normalized cuts and image segmentation","volume":"22","author":"Shi","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1109\/TPAMI.2009.96","article-title":"Turbopixels: Fast superpixels using geometric flows","volume":"31","author":"Levinshtein","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC superpixels compared to state-of-the-art superpixel methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Agoes, A.S., Hu, Z., and Matsunaga, N. (2016). DSLIC: A superpixel based segmentation algorithm for depth image. Proceedings of the Asian Conference on Computer Vision, Springer.","key":"ref_11","DOI":"10.1007\/978-3-319-54427-4_6"},{"key":"ref_12","first-page":"2558","article-title":"RGBD image co-segmentation via saliency detection and graph cut","volume":"30","author":"Li","year":"2018","journal-title":"J. Syst. Simul."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1109\/TPAMI.2015.2465960","article-title":"Hierarchical image saliency detection on extended CSSD","volume":"38","author":"Shi","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9165","DOI":"10.1109\/TIP.2020.3023774","article-title":"Hierarchical feature fusion network for salient object detection","volume":"29","author":"Li","year":"2020","journal-title":"IEEE Trans. Image Process."},{"doi-asserted-by":"crossref","unstructured":"Abouelregal, A.E., and Marin, M. (2020). The size-dependent thermoelastic vibrations of nanobeams subjected to harmonic excitation and rectified sine wave heating. Mathematics, 8.","key":"ref_15","DOI":"10.3390\/math8071128"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1140\/epjs\/s11734-021-00409-1","article-title":"Hybrid nanofluid flow towards an elastic surface with tantalum and nickel nanoparticles, under the influence of an induced magnetic field","volume":"231","author":"Zhang","year":"2022","journal-title":"Eur. Phys. J. Spec. Top."},{"unstructured":"Qin, Y., Lu, H., Xu, Y., and Wang, H. (2015, January 7\u201312). Saliency detection via cellular automata. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","key":"ref_17"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3176","DOI":"10.1109\/TIP.2015.2440174","article-title":"Inner and inter label propagation: Salient object detection in the wild","volume":"24","author":"Li","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.neucom.2018.09.081","article-title":"Graph model-based salient object detection using objectness and multiple saliency cues","volume":"323","author":"Ji","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1683","DOI":"10.1007\/s00371-019-01637-2","article-title":"A novel multi-graph framework for salient object detection","volume":"35","author":"Lu","year":"2019","journal-title":"Vis. Comput."},{"doi-asserted-by":"crossref","unstructured":"Li, X., Li, Y., Shen, C., Dick, A., and Van Den Hengel, A. (2013, January 1\u20138). Contextual hypergraph modeling for salient object detection. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","key":"ref_21","DOI":"10.1109\/ICCV.2013.413"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"29444","DOI":"10.1109\/ACCESS.2018.2797880","article-title":"Saliency detection method using hypergraphs on adaptive multiscales","volume":"6","author":"Han","year":"2018","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"26729","DOI":"10.1109\/ACCESS.2018.2834545","article-title":"Hypergraph optimization for salient region detection based on foreground and background queries","volume":"6","author":"Zhang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.neucom.2017.05.047","article-title":"Kernel quaternion principal component analysis and its application in RGB-D object recognition","volume":"266","author":"Chen","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1109\/LSP.2020.2989674","article-title":"Triple-complementary network for RGB-D salient object detection","volume":"27","author":"Huang","year":"2020","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4296","DOI":"10.1109\/TIP.2020.2968250","article-title":"Improved saliency detection in RGB-D images using two-phase depth estimation and selective deep fusion","volume":"29","author":"Chen","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.neucom.2021.04.053","article-title":"Multi-level cross-modal interaction network for RGB-D salient object detection","volume":"452","author":"Huang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.neucom.2020.12.071","article-title":"Depth quality-aware selective saliency fusion for RGB-D image salient object detection","volume":"432","author":"Wang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1109\/LSP.2013.2292873","article-title":"Co-saliency detection based on hierarchical segmentation","volume":"21","author":"Liu","year":"2013","journal-title":"IEEE Signal Process. Lett."},{"doi-asserted-by":"crossref","unstructured":"Jiang, R., and Crookes, D. (2014, January 27\u201331). Deep salience: Visual salience modeling via deep belief propagation. Proceedings of the AAAI Conference on Artificial Intelligence, Qu\u00e9bec City, QC, Canada.","key":"ref_30","DOI":"10.1609\/aaai.v28i1.9142"},{"doi-asserted-by":"crossref","unstructured":"Lee, M., Park, C., Cho, S., and Lee, S. (2022, January 16\u201319). Superpixel Group-Correlation Network for Co-Saliency Detection. Proceedings of the 2022 IEEE International Conference on Image Processing (ICIP), IEEE, Bordeaux, France.","key":"ref_31","DOI":"10.1109\/ICIP46576.2022.9897408"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"109356","DOI":"10.1016\/j.knosys.2022.109356","article-title":"Co-saliency detection with intra-group two-stage group semantics propagation and inter-group contrastive learning","volume":"252","author":"Tan","year":"2022","journal-title":"Knowl.-Based Syst."},{"doi-asserted-by":"crossref","unstructured":"Zhang, D., Han, J., Li, C., and Wang, J. (2015, January 7\u201312). Co-saliency detection via looking deep and wide. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","key":"ref_33","DOI":"10.1109\/CVPR.2015.7298918"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3766","DOI":"10.1109\/TIP.2013.2260166","article-title":"Cluster-based co-saliency detection","volume":"22","author":"Fu","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1109\/TCYB.2017.2771488","article-title":"An iterative co-saliency framework for RGBD images","volume":"49","author":"Cong","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1109\/TIP.2017.2763819","article-title":"Co-saliency detection for RGBD images based on multi-constraint feature matching and cross label propagation","volume":"27","author":"Cong","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_37","first-page":"2277","article-title":"RGBD Image Co-saliency Object Detection Based on Sample Selection","volume":"42","author":"Liu","year":"2020","journal-title":"Electron. Inf. Technol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"033019","DOI":"10.1117\/1.JEI.28.3.033019","article-title":"Local-linear-fitting-based matting for joint hole filling and depth upsampling of RGB-D images","volume":"28","author":"Zhang","year":"2019","journal-title":"J. Electron. Imaging"},{"doi-asserted-by":"crossref","unstructured":"Xie, S., and Tu, Z. (2015, January 7\u201313). Holistically-nested edge detection. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","key":"ref_39","DOI":"10.1109\/ICCV.2015.164"},{"doi-asserted-by":"crossref","unstructured":"Sasaki, T., Fukushima, N., Maeda, Y., Sugimoto, K., and Kamata, S.I. (2020, January 6\u20138). Constant-time gaussian filtering for acceleration of structure similarity. Proceedings of the 2020 International Conference on Image Processing and Robotics (ICIP), IEEE, Negombo, Sri Lanka.","key":"ref_40","DOI":"10.1109\/ICIP48927.2020.9367337"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3156","DOI":"10.1109\/TIP.2017.2670143","article-title":"Video saliency detection via spatial-temporal fusion and low-rank coherency diffusion","volume":"26","author":"Chen","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2073140","DOI":"10.1155\/2020\/2073140","article-title":"Salient object detection based on weighted hypergraph and random walk","volume":"2020","author":"Wei","year":"2020","journal-title":"Math. Probl. Eng."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/11\/2393\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:16:51Z","timestamp":1760145411000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/14\/11\/2393"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,12]]},"references-count":42,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["sym14112393"],"URL":"https:\/\/doi.org\/10.3390\/sym14112393","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2022,11,12]]}}}