Laplace gradient based Discriminative and Contrast Invertible descriptor

The performance of local descriptors such as SIFT drops under severe illumination changes. In this paper, we propose a Discriminative and Contrast Invertible (DCI) local feature descriptor. In order to increase the discriminative ability of the descriptor under illumination changes, a Laplace gradie...

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Bibliographic Details
Published in2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 1842 - 1846
Main Authors Zhenwei Miao, Kim-Hui Yap, Xudong Jiang, Sinduja, Subbhuraam, Zhenhua Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2017
Subjects
contrast-invertible
Face
face recognition
Histograms
image retrieval
Laplace equations
Lighting
logo search
Robustness
SIFT descriptor
Visualization
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Summary:The performance of local descriptors such as SIFT drops under severe illumination changes. In this paper, we propose a Discriminative and Contrast Invertible (DCI) local feature descriptor. In order to increase the discriminative ability of the descriptor under illumination changes, a Laplace gradient based histogram is proposed. Moreover, a robust contrast flipping estimate is proposed based on the divergence of a local region. Experiments on fine-grained object recognition and retrieval applications demonstrate the superior performance of the DCI descriptor to others.
ISSN:2379-190X
DOI:10.1109/ICASSP.2017.7952475