Making use of the most expressive jumping emerging patterns for classification
Knowledge and Information Systems
Discovering Association Rules Based on Image Content
ADL '99 Proceedings of the IEEE Forum on Research and Technology Advances in Digital Libraries
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Bayesian Hierarchical Model for Learning Natural Scene Categories
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Mining border descriptions of emerging patterns from dataset pairs
Knowledge and Information Systems
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Mining spatial association rules in image databases
Information Sciences: an International Journal
Efficient Mining of Jumping Emerging Patterns with Occurrence Counts for Classification
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
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In this paper we propose a novel method of scene classification, based on the idea of mining emerging patterns between classes of images, represented in a symbolic manner. We use the 9DLT (Direction Lower Triangular) representation of images, which allows to describe scenes with a limited number of symbols, while still capturing spatial relationships between objects visible on the images. We show an efficient method of mining the proposed Spatial Emerging Patterns and present results of synthetic image classification experiments.