A model-based method for rotation invariant texture classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gaussian MRF Rotation-Invariant Features for Image Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Basic Gray Level Aura Matrices: Theory and its Application to Texture Synthesis
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Learning similarity measure for natural image retrieval with relevance feedback
IEEE Transactions on Neural Networks
The gray level aura matrices for textured image segmentation
Analog Integrated Circuits and Signal Processing
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Aurora is the typical ionosphere track generated by the interaction of solar wind and magnetosphere, whose modality and variation are significant to the study of space weather activity. This paper proposes a novel aurora pattern recognition method based on static image classification of day-side aurora. In the feature extraction phase, X-gray level aura matrices (X-GLAMs) are designed to extract the feature of the original aurora images. For classification, models of texture classes are learned using support vector machine (SVM), then a given texture of aurora image can be classified into one of the pre-learned classes. It compares two sets of features: X-GLAMs and basic gray level aura matrices (BGLAMs), both of which are based on different windows on the real aurora image database from Chinese Arctic Yellow River Station. The experimental results illustrate the effectiveness of the proposed dayside aurora classification algorithm.