{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T23:35:34Z","timestamp":1743118534530,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030645823"},{"type":"electronic","value":"9783030645830"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-64583-0_12","type":"book-chapter","created":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T12:10:48Z","timestamp":1610021448000},"page":"113-124","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Novel Reconstruction Errors for Saliency Detection in Hyperspectral Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6226-8967","authenticated-orcid":false,"given":"Antonella","family":"Falini","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cristiano","family":"Tamborrino","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Graziano","family":"Castellano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1072-9578","authenticated-orcid":false,"given":"Francesca","family":"Mazzia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0230-1435","authenticated-orcid":false,"given":"Rosa Maria","family":"Mininni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9840-844X","authenticated-orcid":false,"given":"Annalisa","family":"Appice","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8432-4608","authenticated-orcid":false,"given":"Donato","family":"Malerba","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,8]]},"reference":[{"key":"12_CR1","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/978-3-030-59491-6_15","volume-title":"Foundations of Intelligent Systems","author":"A Appice","year":"2020","unstructured":"Appice, A., Lomuscio, F., Falini, A., Tamborrino, C., Mazzia, F., Malerba, D.: Saliency detection in hyperspectral images using autoencoder-based data reconstruction. In: Helic, D., Leitner, G., Stettinger, M., Felfernig, A., Ra\u015b, Z.W. (eds.) ISMIS 2020. LNCS (LNAI), vol. 12117, pp. 161\u2013170. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59491-6_15"},{"issue":"12","key":"12_CR2","doi-asserted-by":"publisher","first-page":"5706","DOI":"10.1109\/TIP.2015.2487833","volume":"24","author":"A Borji","year":"2015","unstructured":"Borji, A., Cheng, M.M., Jiang, H., Li, J.: Salient object detection: a benchmark. IEEE Trans. Image Process. 24(12), 5706\u20135722 (2015)","journal-title":"IEEE Trans. Image Process."},{"key":"12_CR3","doi-asserted-by":"crossref","unstructured":"Falini, A., et al.: Saliency detection for hyperspectral images via sparse-non negative-matrix-factorization and novel distance measures. In: 2020 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS). pp. 1\u20138. IEEE (2020)","DOI":"10.1109\/EAIS48028.2020.9122749"},{"key":"12_CR4","unstructured":"Gao, D., Vasconcelos, N.: Discriminant saliency for visual recognition from cluttered scenes. In: Advances in Neural Information Processing Systems. pp. 481\u2013488 (2005)"},{"issue":"8","key":"12_CR5","first-page":"1309","volume":"25","author":"J Han","year":"2014","unstructured":"Han, J., Zhang, D., Hu, X., Guo, L., Ren, J., Wu, F.: Background prior-based salient object detection via deep reconstruction residual. IEEE Trans. Circuits Syst. Video Technol. 25(8), 1309\u20131321 (2014)","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"12_CR6","doi-asserted-by":"crossref","unstructured":"Imamoglu, N., et al.: Hyperspectral image dataset for benchmarking on salient object detection. In: 0th International Conference on Quality of Multimedia Experience (QoMEX) (2018)","DOI":"10.1109\/QoMEX.2018.8463428"},{"key":"12_CR7","doi-asserted-by":"crossref","unstructured":"Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. In: Matters of intelligence, pp. 115\u2013141. Springer (1987)","DOI":"10.1007\/978-94-009-3833-5_5"},{"issue":"2\u20133","key":"12_CR8","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/0034-4257(93)90013-N","volume":"44","author":"FA Kruse","year":"1993","unstructured":"Kruse, F.A., et al.: The spectral image processing system (SIPS)\u2014interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ. 44(2\u20133), 145\u2013163 (1993)","journal-title":"Remote Sens. Environ."},{"key":"12_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1007\/978-3-642-03767-2_37","volume-title":"Computer Analysis of Images and Patterns","author":"J Liu","year":"2009","unstructured":"Liu, J., Liu, Y.: A model for saliency detection using NMFsc algorithm. In: Jiang, X., Petkov, N. (eds.) CAIP 2009. LNCS, vol. 5702, pp. 301\u2013308. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-03767-2_37"},{"key":"12_CR10","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"3","key":"12_CR11","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1109\/TIP.2004.838698","volume":"14","author":"RJ Radke","year":"2005","unstructured":"Radke, R.J., Andra, S., Al-Kofahi, O., Roysam, B.: Image change detection algorithms: a systematic survey. IEEE Trans. Image Process. 14(3), 294\u2013307 (2005)","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"12_CR12","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1080\/22797254.2017.1367963","volume":"50","author":"ST Seydi","year":"2017","unstructured":"Seydi, S.T., Hasanlou, M.: A new land-cover match-based change detection for hyperspectral imagery. Euro. J. Remote Sens. 50(1), 517\u2013533 (2017)","journal-title":"Euro. J. Remote Sens."},{"key":"12_CR13","doi-asserted-by":"crossref","unstructured":"Tang, J., Lewis, P.H.: Non-negative matrix factorisation for object class discovery and image auto-annotation. In: Proceedings of the 2008 international conference on Content-based image and video retrieval. pp. 105\u2013112. ACM (2008)","DOI":"10.1145\/1386352.1386370"},{"issue":"6","key":"12_CR14","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1109\/TNNLS.2015.2461554","volume":"27","author":"D Tao","year":"2015","unstructured":"Tao, D., Cheng, J., Song, M., Lin, X.: Manifold ranking-based matrix factorization for saliency detection. IEEE Trans. Neural Networks Learn. Syst. 27(6), 1122\u20131134 (2015)","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"issue":"1","key":"12_CR15","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/0010-0285(80)90005-5","volume":"12","author":"AM Treisman","year":"1980","unstructured":"Treisman, A.M., Gelade, G.: A feature-integration theory of attention. Cogn. Psychol. 12(1), 97\u2013136 (1980)","journal-title":"Cogn. Psychol."},{"key":"12_CR16","doi-asserted-by":"publisher","first-page":"e453","DOI":"10.7717\/peerj.453","volume":"2","author":"S van der Walt","year":"2014","unstructured":"van der Walt, S., et al.: The scikit-image contributors: scikit-image: image processing in Python. Peer J. 2, e453 (2014)","journal-title":"Peer J."},{"key":"12_CR17","unstructured":"Yang, Z., Mueller, R.: Spatial-spectral cross-correlation for change detection\u2013a case study for citrus coverage change detection. In: ASPRS 2007 Annual Conference (2007)"},{"key":"12_CR18","doi-asserted-by":"crossref","unstructured":"Zheng, Q., Yu, S., You, X.: Coarse-to-fine salient object detection with low-rank matrix recovery. Neurocomputing (2019)","DOI":"10.1016\/j.neucom.2019.08.091"},{"key":"12_CR19","first-page":"849","volume":"13","author":"B Zupan","year":"2012","unstructured":"Zupan, B., et al.: Nimfa: A python library for nonnegative matrix factorization. J. Mach. Learn. Res. 13, 849\u2013853 (2012)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-64583-0_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,26]],"date-time":"2021-04-26T19:33:04Z","timestamp":1619465584000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-64583-0_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030645823","9783030645830"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-64583-0_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"8 January 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Siena","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 July 2020","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":"mod2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2020.icas.xyz\/","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":"in-house system and easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"209","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":"116","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":"56% - 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":"5-6","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":"1-2","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)"}}]}}