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Authors: Dawood Al Chanti 1 and Alice Caplier 2

Affiliations: 1 Univ. Grenoble Alpes and CNRS, France ; 2 Grenoble INP, GIPSA-lab and, France

Keyword(s): BoVW, k-means++, Relative Conjunction Matrix, SIFT, Spatial Pyramids, TF.IDF.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Computer Vision, Visualization and Computer Graphics ; Enterprise Information Systems ; Features Extraction ; Human and Computer Interaction ; Human-Computer Interaction ; Image and Video Analysis ; Shape Representation and Matching

Abstract: Bag-of-Visual-Words (BoVW) approach has been widely used in the recent years for image classification purposes. However, the limitations regarding optimal feature selection, clustering technique, the lack of spatial organization of the data and the weighting of visual words are crucial. These factors affect the stability of the model and reduce performance. We propose to develop an algorithm based on BoVW for facial expression analysis which goes beyond those limitations. Thus the visual codebook is built by using k-Means++ method to avoid poor clustering. To exploit reliable low level features, we search for the best feature detector that avoids locating a large number of keypoints which do not contribute to the classification process. Then, we propose to compute the relative conjunction matrix in order to preserve the spatial order of the data by coding the relationships among visual words. In addition, a weighting scheme that reflects how important a visual word is with respect to a given image is introduced. We speed up the learning process by using histogram intersection kernel by Support Vector Machine to learn a discriminative classifier. The efficiency of the proposed algorithm is compared with standard bag of visual words method and with bag of visual words method with spatial pyramid. Extensive experiments on the CK+, the MMI and the JAFFE databases show good average recognition rates. Likewise, the ability to recognize spontaneous and non-basic expressive states is investigated using the DynEmo database. (More)

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Paper citation in several formats:
Al Chanti, D. and Caplier, A. (2018). Improving Bag-of-Visual-Words Towards Effective Facial Expressive Image Classification. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP; ISBN 978-989-758-290-5; ISSN 2184-4321, SciTePress, pages 145-152. DOI: 10.5220/0006537601450152

@conference{visapp18,
author={Dawood {Al Chanti}. and Alice Caplier.},
title={Improving Bag-of-Visual-Words Towards Effective Facial Expressive Image Classification},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP},
year={2018},
pages={145-152},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006537601450152},
isbn={978-989-758-290-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 5: VISAPP
TI - Improving Bag-of-Visual-Words Towards Effective Facial Expressive Image Classification
SN - 978-989-758-290-5
IS - 2184-4321
AU - Al Chanti, D.
AU - Caplier, A.
PY - 2018
SP - 145
EP - 152
DO - 10.5220/0006537601450152
PB - SciTePress