{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T14:35:38Z","timestamp":1758810938998,"version":"3.37.3"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T00:00:00Z","timestamp":1686873600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T00:00:00Z","timestamp":1686873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1934388"],"award-info":[{"award-number":["1934388"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1840761"],"award-info":[{"award-number":["1840761"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012025","name":"New York University Abu Dhabi","doi-asserted-by":"publisher","award":["CG001"],"award-info":[{"award-number":["CG001"]}],"id":[{"id":"10.13039\/100012025","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Film post-production can be time- and money-inefficient. The reason is that a lot of the work involves a person or group of people, called metadata taggers, going through each individual piece of media and marking it up with relevant tags, such as the scene number, transcripts, and the type of shot for video footage. Such a task is particularly time-consuming for films with high shooting ratios (i.e., footage shot\/footage shown). AutoTag automates much of the tagging process across 16 languages, saving both time and money. We describe the algorithms and implementation of AutoTag and report on some case studies.<\/jats:p>","DOI":"10.1007\/s11042-023-15565-w","type":"journal-article","created":{"date-parts":[[2023,6,16]],"date-time":"2023-06-16T11:02:28Z","timestamp":1686913348000},"page":"6731-6753","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["AutoTag: automated metadata tagging for film post-production"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3227-9195","authenticated-orcid":false,"given":"Marcelo","family":"Sandoval-Casta\u00f1eda","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Scandar","family":"Copti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dennis","family":"Shasha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,16]]},"reference":[{"key":"15565_CR1","unstructured":"Audio Video Sync Software Red Giant - PluralEyes by Maxon. https:\/\/www.maxon.net\/en\/red-giant\/pluraleyes"},{"key":"15565_CR2","doi-asserted-by":"crossref","unstructured":"Bansal S, Kamper H, Livescu K, Lopez A, Goldwater S (2018) Low-resource speech-to-text translation. Computing Research Repository","DOI":"10.21437\/Interspeech.2018-1326"},{"key":"15565_CR3","unstructured":"Berard A, Pietquin O, Servan C, Besacier L (2016) Listen and translate: a proof of concept for end-to-end speech-to-text translation. Computing Research Repository"},{"key":"15565_CR4","doi-asserted-by":"crossref","unstructured":"Canini L, Benini S, Leonardi R (2011) Classifying cinematographic shot types. Multimedia Tools and Applications","DOI":"10.1007\/s11042-011-0916-9"},{"key":"15565_CR5","volume-title":"Xu, C.","author":"MA Hasan","year":"2014","unstructured":"Hasan MA, Xu M, He X (2014) Xu, C. Camera motion histogram descriptor and its application to cinematographic shot classification. IEEE Transactions on Circuits and Systems for Video Technology, Camhid"},{"key":"15565_CR6","doi-asserted-by":"crossref","unstructured":"Jang Y, Gunes H, Patras I (2019) Registration-free face-ssd: single shot analysis of smiles, facial attributes, and affect in the wild Computer Vision and Image Understanding","DOI":"10.1016\/j.cviu.2019.01.006"},{"key":"15565_CR7","doi-asserted-by":"crossref","unstructured":"Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S, Darrell T (2014) Caffe: convolutional architecture for fast feature embedding. Proceedings of the 22nd ACM international conference on Multimedia","DOI":"10.1145\/2647868.2654889"},{"key":"15565_CR8","doi-asserted-by":"crossref","unstructured":"Kepuska V, Bohouta G (2017) Comparing speech recognition systems (microsoft api, google api and cmu sphinx). International Journal of Engineering Research and Application","DOI":"10.9790\/9622-0703022024"},{"key":"15565_CR9","unstructured":"Khilari P, Bhope VP (2015) A review on speech to text conversion methods. International Journal of Advanced Research in Computer Engineering & Technology"},{"key":"15565_CR10","doi-asserted-by":"crossref","unstructured":"Loper E, Bird S (2002) Nltk: the natural language toolkit. Proceedings of the ACL-02 workshop on effective tools and methodologies for teaching natural language processing and computational linguistics - vol 1","DOI":"10.3115\/1118108.1118117"},{"key":"15565_CR11","unstructured":"Masingarbe Q (2019). https:\/\/github.com\/qmasingarbe\/pymiere"},{"key":"15565_CR12","doi-asserted-by":"crossref","unstructured":"Neculoiu P, Versteegh M, Rotaru M (2016) Learning text similarity with siamese recurrent networks Proceedings of the 1st Workshop on Representation Learning for NLP","DOI":"10.18653\/v1\/W16-1617"},{"key":"15565_CR13","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in python. Journal of Machine Learning Research"},{"key":"15565_CR14","unstructured":"Pro Media Tools. https:\/\/www.digitalrebellion.com\/promedia\/"},{"key":"15565_CR15","unstructured":"Rosebrock A (2018) Face detection with opencv and deep learning. PyImageSearch"},{"key":"15565_CR16","unstructured":"Shakhovska N, Basystiuk O, Shakhovska K (2019) Development of the speech-to-text chatbot interface based on google api. Modern Machine Learning Technologies"},{"key":"15565_CR17","unstructured":"Speech to text in premiere pro. https:\/\/helpx.adobe.com\/premiere-pro\/using\/speech-to-text.html"},{"key":"15565_CR18","doi-asserted-by":"crossref","unstructured":"Tang X, Du DK, He Z, Liu J (2018) Pyramidbox: a context-assisted single shot face detector. European Conference on Computer Vision","DOI":"10.1007\/978-3-030-01240-3_49"},{"key":"15565_CR19","unstructured":"Transcribe, search and edit in premiere pro. https:\/\/transcriptive.com\/products\/transcriptive-for-premiere-pro\/"},{"key":"15565_CR20","unstructured":"Tsingalis I, Vretos N, Nikolaidis N, Pitas I (2012) Svm-based shot type classification of movie content. IEEE Mediterranean Electrotechnical Conference"},{"key":"15565_CR21","doi-asserted-by":"crossref","unstructured":"Ukkonen E (1992) Approximate string-matching with q-grams and maximal matches. Theoretical Computer Science","DOI":"10.1016\/0304-3975(92)90143-4"},{"key":"15565_CR22","unstructured":"Wang S, Xu T, Li W, Sun H (2017) Cssd: cascade single shot face detector. IEEE International Conference on Image Processing"},{"key":"15565_CR23","unstructured":"Yang S, Xiong Y, Loy CC, Tang X (2017) Face detection through scale-friendly deep convolutional networks. Computing Research Repository"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15565-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-15565-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15565-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T07:32:10Z","timestamp":1704699130000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-15565-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,16]]},"references-count":23,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["15565"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-15565-w","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2023,6,16]]},"assertion":[{"value":"9 August 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 October 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}