Semi-supervised and Active Learning in Video Scene Classification from Statistical Features

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F18%3A00324678" target="_blank" >RIV/68407700:21240/18:00324678 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Semi-supervised and Active Learning in Video Scene Classification from Statistical Features

Popis výsledku v původním jazyce

In multimedia classification, the background is usually con- sidered an unwanted part of input data and is often modeled only to be removed in later processing. Contrary to that, we believe that a back- ground 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 follow- up 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 ap- proach, we are currently able to achieve precision over 80% on a dataset trained on a single series of a popular TV show.

Název v anglickém jazyce

Semi-supervised and Active Learning in Video Scene Classification from Statistical Features

Popis výsledku anglicky

In multimedia classification, the background is usually con- sidered an unwanted part of input data and is often modeled only to be removed in later processing. Contrary to that, we believe that a back- ground 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 follow- up 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 ap- proach, we are currently able to achieve precision over 80% on a dataset trained on a single series of a popular TV show.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA18-18080S" target="_blank" >GA18-18080S: Objevování znalostí v datech o aktivitě člověka založené na fúzi</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the Workshop on Interactive Adaptive Learning (IAL 2018) co-located with European Conference on Machine Learning (ECML 2018) and Principles and Practice of Knowledge Discovery in Databases (PKDD 2018)

ISBN

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ISSN

1613-0073

e-ISSN

1613-0073

Počet stran výsledku

12

Strana od-do

24-35

Název nakladatele

CEUR Workshop Proceedings

Místo vydání

Aachen

Místo konání akce

Dublin

Datum konání akce

10. 9. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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