Pleasant/Unpleasant Filtering for Affective Image Retrieval Based on Cross-Correlation of EEG Features
People often make decisions based on sensitivity rather than rationality. In the field of biological information processing, methods are available for analyzing biological information directly based on electroencephalogram: EEG to determine the pleasant/unpleasant reactions of users. In this study,...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2014/415187 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832559234572615680 |
|---|---|
| author | Keranmu Xielifuguli Akira Fujisawa Yusuke Kusumoto Kazuyuki Matsumoto Kenji Kita |
| author_facet | Keranmu Xielifuguli Akira Fujisawa Yusuke Kusumoto Kazuyuki Matsumoto Kenji Kita |
| author_sort | Keranmu Xielifuguli |
| collection | DOAJ |
| description | People often make decisions based on sensitivity rather than rationality. In the field of biological information processing, methods are available for analyzing biological information directly based on electroencephalogram: EEG to determine the pleasant/unpleasant reactions of users. In this study, we propose a sensitivity filtering technique for discriminating preferences (pleasant/unpleasant) for images using a sensitivity image filtering system based on EEG. Using a set of images retrieved by similarity retrieval, we perform the sensitivity-based pleasant/unpleasant classification of images based on the affective features extracted from images with the maximum entropy method: MEM. In the present study, the affective features comprised cross-correlation features obtained from EEGs produced when an individual observed an image. However, it is difficult to measure the EEG when a subject visualizes an unknown image. Thus, we propose a solution where a linear regression method based on canonical correlation is used to estimate the cross-correlation features from image features. Experiments were conducted to evaluate the validity of sensitivity filtering compared with image similarity retrieval methods based on image features. We found that sensitivity filtering using color correlograms was suitable for the classification of preferred images, while sensitivity filtering using local binary patterns was suitable for the classification of unpleasant images. Moreover, sensitivity filtering using local binary patterns for unpleasant images had a 90% success rate. Thus, we conclude that the proposed method is efficient for filtering unpleasant images. |
| format | Article |
| id | doaj-art-aceee6d7676448b3a21235ef4a6cba93 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-aceee6d7676448b3a21235ef4a6cba932025-02-03T01:30:35ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322014-01-01201410.1155/2014/415187415187Pleasant/Unpleasant Filtering for Affective Image Retrieval Based on Cross-Correlation of EEG FeaturesKeranmu Xielifuguli0Akira Fujisawa1Yusuke Kusumoto2Kazuyuki Matsumoto3Kenji Kita4The University of Tokushima 2-1 Minami-Josanjima, Tokushima 770-8506, JapanThe University of Tokushima 2-1 Minami-Josanjima, Tokushima 770-8506, JapanThe University of Tokushima 2-1 Minami-Josanjima, Tokushima 770-8506, JapanThe University of Tokushima 2-1 Minami-Josanjima, Tokushima 770-8506, JapanThe University of Tokushima 2-1 Minami-Josanjima, Tokushima 770-8506, JapanPeople often make decisions based on sensitivity rather than rationality. In the field of biological information processing, methods are available for analyzing biological information directly based on electroencephalogram: EEG to determine the pleasant/unpleasant reactions of users. In this study, we propose a sensitivity filtering technique for discriminating preferences (pleasant/unpleasant) for images using a sensitivity image filtering system based on EEG. Using a set of images retrieved by similarity retrieval, we perform the sensitivity-based pleasant/unpleasant classification of images based on the affective features extracted from images with the maximum entropy method: MEM. In the present study, the affective features comprised cross-correlation features obtained from EEGs produced when an individual observed an image. However, it is difficult to measure the EEG when a subject visualizes an unknown image. Thus, we propose a solution where a linear regression method based on canonical correlation is used to estimate the cross-correlation features from image features. Experiments were conducted to evaluate the validity of sensitivity filtering compared with image similarity retrieval methods based on image features. We found that sensitivity filtering using color correlograms was suitable for the classification of preferred images, while sensitivity filtering using local binary patterns was suitable for the classification of unpleasant images. Moreover, sensitivity filtering using local binary patterns for unpleasant images had a 90% success rate. Thus, we conclude that the proposed method is efficient for filtering unpleasant images.http://dx.doi.org/10.1155/2014/415187 |
| spellingShingle | Keranmu Xielifuguli Akira Fujisawa Yusuke Kusumoto Kazuyuki Matsumoto Kenji Kita Pleasant/Unpleasant Filtering for Affective Image Retrieval Based on Cross-Correlation of EEG Features Applied Computational Intelligence and Soft Computing |
| title | Pleasant/Unpleasant Filtering for Affective Image Retrieval Based on Cross-Correlation of EEG Features |
| title_full | Pleasant/Unpleasant Filtering for Affective Image Retrieval Based on Cross-Correlation of EEG Features |
| title_fullStr | Pleasant/Unpleasant Filtering for Affective Image Retrieval Based on Cross-Correlation of EEG Features |
| title_full_unstemmed | Pleasant/Unpleasant Filtering for Affective Image Retrieval Based on Cross-Correlation of EEG Features |
| title_short | Pleasant/Unpleasant Filtering for Affective Image Retrieval Based on Cross-Correlation of EEG Features |
| title_sort | pleasant unpleasant filtering for affective image retrieval based on cross correlation of eeg features |
| url | http://dx.doi.org/10.1155/2014/415187 |
| work_keys_str_mv | AT keranmuxielifuguli pleasantunpleasantfilteringforaffectiveimageretrievalbasedoncrosscorrelationofeegfeatures AT akirafujisawa pleasantunpleasantfilteringforaffectiveimageretrievalbasedoncrosscorrelationofeegfeatures AT yusukekusumoto pleasantunpleasantfilteringforaffectiveimageretrievalbasedoncrosscorrelationofeegfeatures AT kazuyukimatsumoto pleasantunpleasantfilteringforaffectiveimageretrievalbasedoncrosscorrelationofeegfeatures AT kenjikita pleasantunpleasantfilteringforaffectiveimageretrievalbasedoncrosscorrelationofeegfeatures |