Semi-supervised and Active Learning in Video Scene Classification from Statistical Features
The result's identifiers
Result code in 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>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Semi-supervised and Active Learning in Video Scene Classification from Statistical Features
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA18-18080S" target="_blank" >GA18-18080S: Fusion-Based Knowledge Discovery in Human Activity Data</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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
Number of pages
12
Pages from-to
24-35
Publisher name
CEUR Workshop Proceedings
Place of publication
Aachen
Event location
Dublin
Event date
Sep 10, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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