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Semi-supervised and Active Learning in Video Scene Classification from Statistical Features
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SYSNO ASEP 0493293 Document Type C - Proceedings Paper (int. conf.) R&D Document Type O - Ostatní Title Semi-supervised and Active Learning in Video Scene Classification from Statistical Features Author(s) Šabata, T. (CZ)
Pulc, Petr (UIVT-O) SAI, ORCID
Holeňa, Martin (UIVT-O) SAI, RIDSource Title ECML PKDD 2018: Workshop on Interactive Adaptive Learning. Proceedings. - Dublin, 2018 / Krempl G. ; Lemaire V. ; Kottke D. ; Calma A. ; Holzinger A. ; Polikar R. ; Sick B. Pages s. 24-35 Number of pages 12 s. Publication form Online - E Action ECML PKDD 2018: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Event date 10.09.2018 - 14.09.2018 VEvent location Dublin Country IE - Ireland Event type EUR Language eng - English Country IE - Ireland Keywords video data ; scene classification ; semi-supervised learning ; active learning ; colour statistics ; feedforward neural networks Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects GA18-18080S GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 Annotation PUBLISHED: ECML PKDD 2018: Workshop on Interactive Adaptive Learning. Proceedings. Dublin, 2018 - (Krempl, G., Lemaire, V., Kottke, D., Calma, A., Holzinger, A., Polikar, R., Sick, B.), s. 24-35. [ECML PKDD 2018: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Dublin (IE), 10.09.2018-14.09.2018]. Grant CEP: GA ČR(CZ) GA18-18080S. ABSTRACT: In multimedia classification, the background is usually considered an unwanted part of input data and is often modeled only to be removed in later processing. Contrary to that, we believe that a background model (i.e., the scene in which the picture or video shot is taken) should be included as an essential feature for both indexing and followup content processing. Information about image background, however, is not usually the main target in the labeling process and the number of annotated samples is very limited. Therefore, we propose to use a combination of semi-supervised and active learning to improve the performance of our scene classifier, specifically a combination of self-training with uncertainty sampling. As a result, we utilize a combination of statistical features extractor, a feed-forward neural network and support vector machine classifier, which consistently achieves higher accuracy on less diverse data. With the proposed approach, we are currently able to achieve precision over 80% on a dataset trained on a single series of a popular TV show. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2019 Electronic address ttps://www.ies.uni-kassel.de/p/ial2018/ialatecml2018.pdf
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