Improving Image Classification Quality Via Dissimilarity Measure In Non-Euclidean Spaces

DOI

10.2991/isci-15.2015.91How to use a DOI?

Keywords

Dissimilarity Increment Distribution; High-Order Statistics; Maximum A Posteriori; Gaussian Mixture Model; Non-Euclidean Space

Abstract

This paper proposes an image classification scheme by learning the dissimilarity measure in non-Euclidean spaces. Specifically, the dissimilarity representations of samples from a pseudo-Euclidean space are first constructed; then, the dissimilarity increment distribution information of each category is achieved with respect to the high-order statistics of triplet-neighbor points for each image; finally, a maximum a posteriori algorithm fused with the Gaussian Mixture Model and triplet-dissimilarity increments distribution is utilized to estimate the relevance of each image category with each input image. Experimental results conducted on a general image database demonstrate the effectiveness of the proposed scheme.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lingling Chen
AU  - Songhao Zhu
PY  - 2015/01
DA  - 2015/01
TI  - Improving Image Classification Quality Via Dissimilarity Measure In Non-Euclidean Spaces
BT  - Proceedings of the 2015 International Symposium on Computers & Informatics
PB  - Atlantis Press
SP  - 682
EP  - 688
SN  - 2352-538X
UR  - https://doi.org/10.2991/isci-15.2015.91
DO  - 10.2991/isci-15.2015.91
ID  - Chen2015/01
ER  -