{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T08:56:37Z","timestamp":1765356997745,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030873608"},{"type":"electronic","value":"9783030873615"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-87361-5_23","type":"book-chapter","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T23:54:11Z","timestamp":1632959651000},"page":"277-289","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Robust Image Cropping by Filtering Composition Irrelevant Factors"],"prefix":"10.1007","author":[{"given":"Zhiyu","family":"Pan","sequence":"first","affiliation":[]},{"given":"Ke","family":"Xian","sequence":"additional","affiliation":[]},{"given":"Hao","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Zhiguo","family":"Cao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,30]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","unstructured":"Chen, J., Bai, G., Liang, S., Li, Z.: Automatic image cropping: a computational complexity study. In: CVPR, pp. 507\u2013515 (2016)","DOI":"10.1109\/CVPR.2016.61"},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Chen, Y.L., Huang, T.W., Chang, K.H., Tsai, Y.C., Chen, H.T., Chen, B.Y.: Quantitative analysis of automatic image cropping algorithms: a dataset and comparative study. In: WACV, pp. 226\u2013234. IEEE (2017)","DOI":"10.1109\/WACV.2017.32"},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Chen, Y.L., Klopp, J., Sun, M., Chien, S.Y., Ma, K.L.: Learning to compose with professional photographs on the web. In: MM, pp. 37\u201345 (2017)","DOI":"10.1145\/3123266.3123274"},{"key":"23_CR4","doi-asserted-by":"crossref","unstructured":"Fang, C., Lin, Z., Mech, R., Shen, X.: Automatic image cropping using visual composition, boundary simplicity and content preservation models. In: MM, pp. 1105\u20131108 (2014)","DOI":"10.1145\/2647868.2654979"},{"key":"23_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/978-3-642-41184-7_16","volume-title":"Image Analysis and Processing \u2013 ICIAP 2013","author":"L Greco","year":"2013","unstructured":"Greco, L., La Cascia, M.: Saliency based aesthetic cut of digital images. In: Petrosino, A. (ed.) ICIAP 2013. LNCS, vol. 8157, pp. 151\u2013160. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-41184-7_16"},{"key":"23_CR6","doi-asserted-by":"crossref","unstructured":"Li, D., Wu, H., Zhang, J., Huang, K.: A2-rl: aesthetics aware reinforcement learning for image cropping. In: CVPR, pp. 8193\u20138201 (2018)","DOI":"10.1109\/CVPR.2018.00855"},{"key":"23_CR7","unstructured":"Li, Y., Pirk, S., Su, H., Qi, C.R., Guibas, L.J.: Fpnn: field probing neural networks for 3d data. In: NIPS, pp. 307\u2013315 (2016)"},{"key":"23_CR8","doi-asserted-by":"crossref","unstructured":"Lu, P., Zhang, H., Peng, X., Peng, X.: Aesthetic guided deep regression network for image cropping. Signal Processing: Image Communication, pp. 1\u201310 (2019)","DOI":"10.1016\/j.image.2019.05.010"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Maturana, D., Scherer, S.: Voxnet: a 3d convolutional neural network for real-time object recognition. In: IROS, pp. 922\u2013928. IEEE (2015)","DOI":"10.1109\/IROS.2015.7353481"},{"key":"23_CR10","doi-asserted-by":"crossref","unstructured":"Park, J., Lee, J.Y., Tai, Y.W., Kweon, I.S.: Modeling photo composition and its application to photo re-arrangement. In: ICIP, pp. 2741\u20132744. IEEE (2012)","DOI":"10.1109\/ICIP.2012.6467466"},{"key":"23_CR11","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Litany, O., He, K., Guibas, L.J.: Deep hough voting for 3d object detection in point clouds. In: ICCV, pp. 9277\u20139286 (2019)","DOI":"10.1109\/ICCV.2019.00937"},{"key":"23_CR12","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Liu, W., Wu, C., Su, H., Guibas, L.J.: Frustum pointnets for 3d object detection from rgb-d data. In: CVPR, pp. 918\u2013927 (2018)","DOI":"10.1109\/CVPR.2018.00102"},{"key":"23_CR13","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: deep learning on point sets for 3d classification and segmentation. In: CVPR, pp. 652\u2013660 (2017)"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Su, H., Nie\u00dfner, M., Dai, A., Yan, M., Guibas, L.J.: Volumetric and multi-view cnns for object classification on 3d data. In: CVPR, pp. 5648\u20135656 (2016)","DOI":"10.1109\/CVPR.2016.609"},{"key":"23_CR15","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. In: NIPS, pp. 5099\u20135108 (2017)"},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Santella, A., Agrawala, M., DeCarlo, D., Salesin, D., Cohen, M.: Gaze-based interaction for semi-automatic photo cropping. In: CHI, pp. 771\u2013780 (2006)","DOI":"10.1145\/1124772.1124886"},{"key":"23_CR17","doi-asserted-by":"crossref","unstructured":"Suh, B., Ling, H., Bederson, B.B., Jacobs, D.W.: Automatic thumbnail cropping and its effectiveness. In: UIST, pp. 95\u2013104 (2003)","DOI":"10.1145\/964696.964707"},{"issue":"4","key":"23_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3450626.3459787","volume":"36","author":"PS Wang","year":"2017","unstructured":"Wang, P.S., Liu, Y., Guo, Y.X., Sun, C.Y., Tong, X.: O-cnn: octree-based convolutional neural networks for 3d shape analysis. TOG 36(4), 1\u201311 (2017)","journal-title":"TOG"},{"key":"23_CR19","doi-asserted-by":"crossref","unstructured":"Wang, W., Shen, J.: Deep cropping via attention box prediction and aesthetics assessment. In: ICCV, pp. 2186\u20132194 (2017)","DOI":"10.1109\/ICCV.2017.240"},{"issue":"7","key":"23_CR20","doi-asserted-by":"publisher","first-page":"1531","DOI":"10.1109\/TPAMI.2018.2840724","volume":"41","author":"W Wang","year":"2018","unstructured":"Wang, W., Shen, J., Ling, H.: A deep network solution for attention and aesthetics aware photo cropping. TPAMI 41(7), 1531\u20131544 (2018)","journal-title":"TPAMI"},{"key":"23_CR21","doi-asserted-by":"crossref","unstructured":"Wei, Z., et al.: Good view hunting: Learning photo composition from dense view pairs. In: CVPR, pp. 5437\u20135446 (2018)","DOI":"10.1109\/CVPR.2018.00570"},{"key":"23_CR22","unstructured":"Wu, Z., et al.: 3d shapenets: a deep representation for volumetric shapes. In: CVPR, pp. 1912\u20131920 (2015)"},{"key":"23_CR23","unstructured":"Zeng, H., Li, L., Cao, Z., Zhang, L.: Grid anchor based image cropping: a new benchmark and an efficient model. arXiv preprint arXiv:1909.08989 (2019)"}],"container-title":["Lecture Notes in Computer Science","Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87361-5_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T00:12:56Z","timestamp":1632960776000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87361-5_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030873608","9783030873615"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87361-5_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"30 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIG","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image and Graphics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Haikou","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 August 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icig2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icig2021.csig.org.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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"421","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":"198","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":"47% - 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","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","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Conference was postponed due to the COVID19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}