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Journal of Electronic Imaging

Adaptive descriptor based on the geometric consistency of local image features: application to flower image classification
Author(s): Asma Najjar; Ezzeddine Zagrouba
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Paper Abstract

Geometric consistency is, usually, considered as a postprocessing step to filter matched sets of local features in order to discard outliers. In this work, it is used to propose an adaptive feature that describes the geometric dispersion of keypoints. It is based on a distribution computed by a nonparametric estimator so that no assumption is made about the data. We investigated and discussed the invariance properties of our descriptor under the most common two- and three-dimensional transformations. Then, we applied it to flower recognition. The classification is performed using the precomputed kernel of support vector machines classifier. Indeed, a similarity computing framework that uses the Kullback–Leibler divergence is presented. Furthermore, a customized layout for each flower image is designed to describe and compare separately the boundary and the central area of flowers. Experimentations made on the Oxford flower-17 dataset prove the efficiency of our method in terms of classification accuracy and computational complexity. The limits of our descriptor are also discussed on a 10-class subset of the Oxford flower-102 dataset.

Paper Details

Date Published: 5 October 2016
PDF: 20 pages
J. Electron. Imaging. 25(5) 053023 doi: 10.1117/1.JEI.25.5.053023
Published in: Journal of Electronic Imaging Volume 25, Issue 5
Show Author Affiliations
Asma Najjar, Univ. of Tunis El Manar (Tunisia)
Ezzeddine Zagrouba, Institut Supérieur d’Informatique (Tunisia)


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