TY - GEN
T1 - Combining image databases for affective image classification
AU - Kim, Hye Rin
AU - Lee, In Kwon
N1 - Publisher Copyright:
Copyright © IARIA, 2015.
Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 2015
Y1 - 2015
N2 - Affective image classification has attracted much attention in recent years. However, the production of more exact classifiers depends on the quality of the sample database. In this study, we analyzed various existing databases used for affective image classification and we tried to improve the quality of the learning data by combining existing databases in several different ways. We found that existing image databases cannot cover the overall range of the arousal-valence plane. Thus, to obtain a wider distribution of emotion labels from images, we conducted a crowd-sourcing-based user study with Amazon Mechanical Turk. We aimed to construct several different versions of affective image classifiers by using different combinations of existing databases, instead of using one. We used low-level features in our classification experiments to explore the discriminatory properties of emotion categories. We report the results of intermediate comparisons using different combinations of databases to evaluate the performance of this approach.
AB - Affective image classification has attracted much attention in recent years. However, the production of more exact classifiers depends on the quality of the sample database. In this study, we analyzed various existing databases used for affective image classification and we tried to improve the quality of the learning data by combining existing databases in several different ways. We found that existing image databases cannot cover the overall range of the arousal-valence plane. Thus, to obtain a wider distribution of emotion labels from images, we conducted a crowd-sourcing-based user study with Amazon Mechanical Turk. We aimed to construct several different versions of affective image classifiers by using different combinations of existing databases, instead of using one. We used low-level features in our classification experiments to explore the discriminatory properties of emotion categories. We report the results of intermediate comparisons using different combinations of databases to evaluate the performance of this approach.
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M3 - Conference contribution
AN - SCOPUS:84966550427
T3 - ACHI 2015 - 8th International Conference on Advances in Computer-Human Interactions
SP - 211
EP - 212
BT - ACHI 2015 - 8th International Conference on Advances in Computer-Human Interactions
A2 - Miller, Leslie
A2 - Culen, Alma Leora
PB - International Academy, Research and Industry Association, IARIA
T2 - 8th International Conference on Advances in Computer-Human Interactions, ACHI 2015
Y2 - 22 February 2015 through 27 February 2015
ER -