Počet záznamů: 1
Semi-supervised and Active Learning in Video Scene Classification from Statistical Features
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SYSNO ASEP 0493293 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV O - Ostatní Název Semi-supervised and Active Learning in Video Scene Classification from Statistical Features Tvůrce(i) Šabata, T. (CZ)
Pulc, Petr (UIVT-O) SAI, ORCID
Holeňa, Martin (UIVT-O) SAI, RIDZdroj.dok. 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. Rozsah stran s. 24-35 Poč.str. 12 s. Forma vydání Online - E Akce ECML PKDD 2018: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Datum konání 10.09.2018 - 14.09.2018 Místo konání Dublin Země IE - Irsko Typ akce EUR Jazyk dok. eng - angličtina Země vyd. IE - Irsko Klíč. slova video data ; scene classification ; semi-supervised learning ; active learning ; colour statistics ; feedforward neural networks Vědní obor RIV IN - Informatika Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) CEP GA18-18080S GA ČR - Grantová agentura ČR Institucionální podpora UIVT-O - RVO:67985807 Anotace 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. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2019 Elektronická adresa ttps://www.ies.uni-kassel.de/p/ial2018/ialatecml2018.pdf
Počet záznamů: 1