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.
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