{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T03:53:56Z","timestamp":1762660436226,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T00:00:00Z","timestamp":1714435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Innovation Key Fund project of Chinese Academy of Sciences","award":["Y8K4160401"],"award-info":[{"award-number":["Y8K4160401"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Finding the most interesting areas of an image is the aim of saliency detection. Conventional methods based on low-level features rely on biological cues like texture and color. These methods, however, have trouble with processing complicated or low-contrast images. In this paper, we introduce a deep neural network-based saliency detection method. First, using semantic segmentation, we construct a pixel-level model that gives each pixel a saliency value depending on its semantic category. Next, we create a region feature model by combining both hand-crafted and deep features, which extracts and fuses the local and global information of each superpixel region. Third, we combine the results from the previous two steps, along with the over-segmented superpixel images and the original images, to construct a multi-level feature model. We feed the model into a deep convolutional network, which generates the final saliency map by learning to integrate the macro and micro information based on the pixels and superpixels. We assess our method on five benchmark datasets and contrast it against 14 state-of-the-art saliency detection algorithms. According to the experimental results, our method performs better than the other methods in terms of F-measure, precision, recall, and runtime. Additionally, we analyze the limitations of our method and propose potential future developments.<\/jats:p>","DOI":"10.3390\/e26050383","type":"journal-article","created":{"date-parts":[[2024,4,30]],"date-time":"2024-04-30T08:14:31Z","timestamp":1714464871000},"page":"383","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Saliency Detection Based on Multiple-Level Feature Learning"],"prefix":"10.3390","volume":"26","author":[{"given":"Xiaoli","family":"Li","sequence":"first","affiliation":[{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Opto-Electronic Information Processing, Shenyang 110169, China"},{"name":"The Key Lab of Image Understanding and Computer Vision, Shenyang 110619, China"},{"name":"School of Computer Science and Engineering, Shenyang Jianzhu University, Shenyang 110168, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunpeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Opto-Electronic Information Processing, Shenyang 110169, China"},{"name":"The Key Lab of Image Understanding and Computer Vision, Shenyang 110619, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7772-8652","authenticated-orcid":false,"given":"Huaici","family":"Zhao","sequence":"additional","affiliation":[{"name":"Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Key Laboratory of Opto-Electronic Information Processing, Shenyang 110169, China"},{"name":"The Key Lab of Image Understanding and Computer Vision, Shenyang 110619, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1109\/TII.2013.2294156","article-title":"GM-PHD-based multi-target visual tracking using entropy distribution and game theory","volume":"10","author":"Zhou","year":"2014","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.neucom.2018.10.089","article-title":"Underwater salient object detection by combining 2D and 3D visual features","volume":"391","author":"Chen","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2315","DOI":"10.1109\/JIOT.2017.2737479","article-title":"Motor anomaly detection for unmanned aerial vehicles using reinforcement learning","volume":"5","author":"Lu","year":"2018","journal-title":"IEEE Int. Things J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8819","DOI":"10.1364\/OE.27.008819","article-title":"Optically guided level set for underwater object segmentation","volume":"27","author":"Chen","year":"2019","journal-title":"Opt. Express"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.ins.2018.12.047","article-title":"Background\u2013foreground interaction for moving object detection in dynamic scenes","volume":"483","author":"Chen","year":"2019","journal-title":"Inf. Sci."},{"doi-asserted-by":"crossref","unstructured":"Yeh, H., and Chen, C. (2012, January 25\u201330). From Rareness to Compactness: Contrast-Aware Image Saliency Detection. Proceedings of the 19th IEEE International Conference on Image Processing, Orlando, FL, USA.","key":"ref_6","DOI":"10.1109\/ICIP.2012.6467050"},{"doi-asserted-by":"crossref","unstructured":"Zhu, W., Liang, S., Wei, Y., and Sun, J. (2014, January 23\u201328). Saliency optimization from robust background detection. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","key":"ref_7","DOI":"10.1109\/CVPR.2014.360"},{"doi-asserted-by":"crossref","unstructured":"Zhang, L., Gu, Z., and Li, H. (2013, January 15\u201318). SDSP: A novel saliency detection method by combining simple priors. Proceedings of the 20th IEEE International Conference on Image Processing, Melbourne, VIC, Australia.","key":"ref_8","DOI":"10.1109\/ICIP.2013.6738036"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/34.730558","article-title":"A model of saliency-based visual attention for rapid scene analysis","volume":"20","author":"Itti","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Achanta, R., Hemami, S., Estrada, F., and Susstrunk, S. (2009, January 20\u201325). Frequency-tuned salient region detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","key":"ref_10","DOI":"10.1109\/CVPRW.2009.5206596"},{"doi-asserted-by":"crossref","unstructured":"Jiang, B., Zhang, L., Lu, H., Yang, C., and Yang, M.H. (2013, January 1\u20138). Saliency detection via absorbing Markov chain. Proceedings of the 2013 IEEE International Conference on Computer Vision, Sydney, NSW, Australia.","key":"ref_11","DOI":"10.1109\/ICCV.2013.209"},{"doi-asserted-by":"crossref","unstructured":"Yang, C., Zhang, L., Lu, H., Ruan, X., and Yang, M. (2013, January 25\u201327). Saliency Detection via Graph-Based Manifold Ranking. Proceedings of the Computer Vision and Pattern Recognition, Portland, OR, USA.","key":"ref_12","DOI":"10.1109\/CVPR.2013.407"},{"doi-asserted-by":"crossref","unstructured":"Li, X., Lu, H., Zhang, L., Ruan, X., and Yang, M.H. (2013, January 1\u20138). Saliency detection via dense and sparse reconstruction. Proceedings of the 2013 IEEE International Conference on Computer Vision, Sydney, NSW, Australia.","key":"ref_13","DOI":"10.1109\/ICCV.2013.370"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1109\/TCSVT.2016.2646720","article-title":"Dense and Sparse Labeling with Multidimensional Features for Saliency Detection","volume":"28","author":"Yuan","year":"2018","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"330","DOI":"10.1007\/s11263-015-0822-0","article-title":"Supercnn: A superpixelwise convolutional neural network for salient object detection","volume":"115","author":"He","year":"2015","journal-title":"Int. J. Comput. Vis."},{"doi-asserted-by":"crossref","unstructured":"Wang, T., Borji, A., Zhang, L., Zhang, P., and Lu, H. (2017, January 24\u201327). A Stagewise Refinement Model for Detecting Salient Objects in Images. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","key":"ref_16","DOI":"10.1109\/ICCV.2017.433"},{"unstructured":"Achanta, R., Shaji, A., Smith, K., Lucchi, A., and S\u00fcsstrunk, S. (2010). Slic Superpixels, EPFL. EPFL Technical Report 149300.","key":"ref_17"},{"unstructured":"Li, G., and Yu, Y. (2015, January 7\u201312). Visual saliency based on multiscale deep features. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","key":"ref_18"},{"doi-asserted-by":"crossref","unstructured":"Wang, L., Lu, H., Zhang, P., and Ruan, X. (2016, January 8\u201316). Saliency detection with recurrent fully convolutional networks. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","key":"ref_19","DOI":"10.1007\/978-3-319-46493-0_50"},{"doi-asserted-by":"crossref","unstructured":"Zhao, R., Ouyang, W., Li, H., and Wang, X. (2015, January 7\u201312). Saliency detection by multi-context deep learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","key":"ref_20","DOI":"10.1109\/CVPR.2015.7298731"},{"doi-asserted-by":"crossref","unstructured":"Hou, Q., Cheng, M.M., Hu, X., Borji, A., Tu, Z., and Torr, P.H. (2017, January 22\u201325). Deeply supervised salient object detection with short connections. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","key":"ref_21","DOI":"10.1109\/CVPR.2017.563"},{"unstructured":"Qin, Y., Feng, M., Lu, H., and Cottrell, G. (2017, January 22\u201325). Hierarchical cellular automata for visual saliency. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","key":"ref_22"},{"key":"ref_23","first-page":"640","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Long","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"doi-asserted-by":"crossref","unstructured":"Feichtinger, H., and Strohmer, T. (1998). Gabor Analysis and Algorithms, Birkhiuser. Theory and Applications.","key":"ref_24","DOI":"10.1007\/978-1-4612-2016-9"},{"unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u201312). Very deep convolutional networks for large-scale image recognition. Proceedings of the Computer Vision and Pattern Recognition, Boston, MA, USA.","key":"ref_25"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4545","DOI":"10.1109\/TIP.2018.2838761","article-title":"An unsupervised game theoretic approach to saliency detection","volume":"27","author":"Zeng","year":"2018","journal-title":"IEEE Trans. Image Process."},{"doi-asserted-by":"crossref","unstructured":"Liu, J.J., Hou, Q., Cheng, M.M., Feng, J., and Jiang, J. (2019, January 18). A simple pooling based design for real-time salient object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","key":"ref_27","DOI":"10.1109\/CVPR.2019.00404"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1109\/TIP.2017.2766787","article-title":"Saliency detection via absorbing Markov chain with learnt transition probability","volume":"27","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"doi-asserted-by":"crossref","unstructured":"Wei, Y., Wen, F., Zhu, W., and Sun, J. (2012, January 7\u201313). Geodesic saliency using back-ground priors. Proceedings of the European Conference on Computer Vision, Florence, Italy.","key":"ref_29","DOI":"10.1007\/978-3-642-33712-3_3"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5706","DOI":"10.1109\/TIP.2015.2487833","article-title":"Salient object detection: A benchmark","volume":"24","author":"Borji","year":"2015","journal-title":"IEEE Trans Image Process."},{"doi-asserted-by":"crossref","unstructured":"Li, Y., Hou, X., Koch, C., Rehg, J., and Yuille, A. (2014, January 23\u201328). The secrets of salient object segmentation. Proceedings of the Computer Vision and Pattern Recognition, Columbus, OH, USA.","key":"ref_31","DOI":"10.1109\/CVPR.2014.43"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.jvcir.2015.03.011","article-title":"An effective vector model for global-contrast-based saliency detection","volume":"30","author":"Xu","year":"2015","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1109\/TPAMI.2014.2345401","article-title":"Global Contrast based Salient Region Detection","volume":"37","author":"Cheng","year":"2015","journal-title":"IEEE Trans Actions Pattern Anal. Mach. Intell."},{"unstructured":"Li, C., Yuan, Y., Cai, W., Xia, Y., and Feng, D. (2015, January 7\u201312). Robust saliency detection via regularized random walks ranking. Proceedings of the Computer Vision and Pattern Recognition, Boston, MA, USA.","key":"ref_34"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/LSP.2013.2260737","article-title":"Graph-regularized saliency detection with convex-hull-based center prior","volume":"20","author":"Yang","year":"2013","journal-title":"IEEE Signal Process. Lett."},{"doi-asserted-by":"crossref","unstructured":"Achanta, R., and S\u00fcsstrunk, S. (2010, January 12\u201315). Saliency detection using maximum symmetric surround. Proceedings of the IEEE International Conference on Image Processing, Hongkong, China.","key":"ref_36","DOI":"10.1109\/ICIP.2010.5652636"},{"doi-asserted-by":"crossref","unstructured":"Li, X., Liu, Y., and Zhao, H. (2023). Image saliency detection based on low-level and high-level features via manifold-space ranking. Electronics, 12.","key":"ref_37","DOI":"10.3390\/electronics12020449"},{"doi-asserted-by":"crossref","unstructured":"Jiang, H., Wang, J., Yuan, Z., Wu, Y., Zheng, N., and Li, S. (2013, January 25\u201327). Salient object detection: A discriminative regional feature integration approach. Proceedings of the Computer Vision and Pattern Recognition, Portland, OR, USA.","key":"ref_38","DOI":"10.1109\/CVPR.2013.271"},{"doi-asserted-by":"crossref","unstructured":"Kim, J., Han, D., Tai, Y., and Kim, J. (2014, January 23\u201328). Salient region detection via high-dimensional color transform. Proceedings of the Computer Vision and Pattern Recognition, Columbus, OH, USA.","key":"ref_39","DOI":"10.1109\/CVPR.2014.118"},{"doi-asserted-by":"crossref","unstructured":"Wang, L., Lu, H., Ruan, X., and Yang, M. (2015, January 7\u201312). Deep networks for saliency detection via local estimation and global search. Proceedings of the Computer Vision and Pattern Recognition, Boston, MA, USA.","key":"ref_40","DOI":"10.1109\/CVPR.2015.7298938"},{"unstructured":"Li, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2019, January 22\u201325). SSD-HS: High-Speed Saliency Detection with Single Shot MultiBox Detector and Random Forest. Proceedings of the IEEE International Conference on Image Processing, Taipei, China.","key":"ref_41"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1599","DOI":"10.1109\/TPAMI.2017.2737631","article-title":"ELD-Net: An Efficient Deep Learning Architecture for Accurate Saliency Detection","volume":"40","author":"Lee","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/26\/5\/383\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:36:43Z","timestamp":1760107003000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/26\/5\/383"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,30]]},"references-count":42,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["e26050383"],"URL":"https:\/\/doi.org\/10.3390\/e26050383","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2024,4,30]]}}}