{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T11:48:39Z","timestamp":1774352919100,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":19,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819985517","type":"print"},{"value":"9789819985524","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:00:00Z","timestamp":1703721600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:00:00Z","timestamp":1703721600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8552-4_16","type":"book-chapter","created":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T07:02:36Z","timestamp":1703660556000},"page":"199-210","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Image Visual Complexity Evaluation Based on Deep Ordinal Regression"],"prefix":"10.1007","author":[{"given":"Xiaoying","family":"Guo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanfeng","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,28]]},"reference":[{"issue":"2","key":"16_CR1","doi-asserted-by":"publisher","first-page":"173","DOI":"10.2190\/EM.28.2.d","volume":"28","author":"M Nadal","year":"2010","unstructured":"Nadal, M., Munar, E., Marty, G., Cela-Conde, C.J.: Visual complexity and beauty appreciation: explaining the divergence of results. Empir. Stud. Arts 28(2), 173\u2013191 (2010)","journal-title":"Empir. Stud. Arts"},{"issue":"4","key":"16_CR2","first-page":"819","volume":"48","author":"X Guo","year":"2020","unstructured":"Guo, X., Li, W., Qian, Y., Bai, R., Jia, C.: A review of computational methods for image complexity assessment. Acta Electron. Sin. 48(4), 819\u2013826 (2020)","journal-title":"Acta Electron. Sin."},{"key":"16_CR3","doi-asserted-by":"publisher","unstructured":"Niu, Z., Zhou, M., Wang, L., Gao, X.: Ordinal regression with multiple output CNN for age estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4920\u20134928 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.532","DOI":"10.1109\/CVPR.2016.532"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"D\u00edaz, R., Marathe, A.: Soft labels for ordinal regression. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4733\u20134742 (2019)","DOI":"10.1109\/CVPR.2019.00487"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Lee, Y.J., Efros, A.A., Hebert, M.: Style-aware mid-level representation for discovering visual connections in space and time. In: Proceedings of the 2013 IEEE International Conference on Computer Vision (ICCV), pp. 1857\u20131864 (2013)","DOI":"10.1109\/ICCV.2013.233"},{"issue":"9","key":"16_CR6","doi-asserted-by":"publisher","first-page":"4398","DOI":"10.1109\/TNNLS.2017.2766164","volume":"29","author":"Y Xiao","year":"2018","unstructured":"Xiao, Y., Liu, B., Hao, Z.: Multiple-instance ordinal regression. IEEE Trans. Neural Netw. Learn. Syst. 29(9), 4398\u20134413 (2018)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"16_CR7","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.neucom.2020.03.034","volume":"401","author":"VM Vargas","year":"2020","unstructured":"Vargas, V.M., Gutierrez, P.A., Hervas-Martinez, C.: Cumulative link models for deep ordinal classification. Neurocomputing 401, 48\u201358 (2020)","journal-title":"Neurocomputing"},{"issue":"7","key":"16_CR8","first-page":"8577","volume":"45","author":"T Feng","year":"2023","unstructured":"Feng, T., et al.: IC9600: a benchmark dataset for automatic image complexity assessment. IEEE Trans. Pattern Anal. Mach. Intell. 45(7), 8577\u20138593 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"16_CR9","doi-asserted-by":"publisher","first-page":"1474","DOI":"10.1016\/j.fss.2008.11.017","volume":"160","author":"M Cardaci","year":"2009","unstructured":"Cardaci, M., Di Ges\u00f9, V., Petrou, M., Tabacchi, M.E.: A fuzzy approach to the evaluation of image complexity. Fuzzy Sets Syst. 160, 1474\u20131484 (2009)","journal-title":"Fuzzy Sets Syst."},{"key":"16_CR10","first-page":"71","volume":"10","author":"S Mayer","year":"2014","unstructured":"Mayer, S., Landwehr, J.: When complexity is symmetric: the interplay of two core determinants of visual aesthetics. Adv. Cogn. Psychol. 10, 71\u201380 (2014)","journal-title":"Adv. Cogn. Psychol."},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Guo, X., Qian, Y., Li, L., Asano, A.: Assessment model for perceived visual complexity of painting images. Knowl.-Based Syst. 159, 110\u2013119 (2018)","DOI":"10.1016\/j.knosys.2018.06.006"},{"issue":"191487","key":"16_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1098\/rsos.191487","volume":"7","author":"F Nagle","year":"2020","unstructured":"Nagle, F., Lavie, N.: Predicting human complexity perception of real-world scienes. R. Soc. Open Sci. 7(191487), 1\u201314 (2020). https:\/\/doi.org\/10.1098\/rsos.191487","journal-title":"R. Soc. Open Sci."},{"key":"16_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cviu.2020.102949","volume":"195","author":"E Saraee","year":"2020","unstructured":"Saraee, E., Jalal, M., Betke, M.: Visual complexity analysis using deep intermediate-layer features. Comput. Vis. Image Understand. 195, 1\u201313 (2020)","journal-title":"Comput. Vis. Image Understand."},{"key":"16_CR14","doi-asserted-by":"publisher","first-page":"105319","DOI":"10.1016\/j.cognition.2022.105319","volume":"231","author":"C Kyle-Davidson","year":"2023","unstructured":"Kyle-Davidson, C., Zhou, E.Y., Walther, D.B., Bors, A.G., Evans, K.K.: Characterising and dissecting human perception of scene complexity. Cognition 231, 105319 (2023)","journal-title":"Cognition"},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Kyle-Davidson, C., Bors, A.G., Evans, K.K.: Predicting human perception of scene complexity. In: IEEE International Conference on Image Processing (ICIP), pp. 1281\u20131285 (2022)","DOI":"10.1109\/ICIP46576.2022.9897953"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Chen, S., Zhang, C., Dong, M., Le, J., Rao, M.: Using ranking-CNN for age estimation. In: Computer Vision and Pattern Recognition (CVPR), pp. 5183\u20135192 (2017)","DOI":"10.1109\/CVPR.2017.86"},{"key":"16_CR17","unstructured":"Cao, W., Mirjalili, V., Raschka S.: Consistent rank logits for ordinal regression with convolutional neural networks. arXiv preprint arXiv:1901.078846 (2019)"},{"key":"16_CR18","unstructured":"Shi, X., Cao, W.: Deep neural networks for rank-consistent ordinal regression based on conditional probabilities. arXiv preprintarXiv:2111.08851 (2021)"},{"key":"16_CR19","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vision 88, 303\u2013338 (2010)","journal-title":"Int. J. Comput. Vision"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8552-4_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T07:14:23Z","timestamp":1703661263000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8552-4_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,28]]},"ISBN":["9789819985517","9789819985524"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8552-4_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,28]]},"assertion":[{"value":"28 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1420","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"532","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,78","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,69","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}