Iconic indexing by 2-D strings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Processing object-oientation-based direction queries: a summary of results
Proceedings of the 8th ACM international symposium on Advances in geographic information systems
Mining frequent neighboring class sets in spatial databases
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Discovering Spatial Co-location Patterns: A Summary of Results
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Mining viewpoint patterns in image databases
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
A partial join approach for mining co-location patterns
Proceedings of the 12th annual ACM international workshop on Geographic information systems
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
Mining Spatial Co-orientation Patterns for Analyzing Portfolios of Spatial Cognitive Development
ICALT '06 Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies
| Hi-index | 12.05 |
Image mining is an important task to discover interesting and meaningful patterns form large image databases. In this paper, we introduce the spatial co-orientation patterns in image databases. Spatial co-orientation patterns refer to objects that frequently occur with the same spatial orientation, e.g. left, right, below, etc. among images. For example, an object P is frequently left to an object Q among images. We utilize the data structure, 2D string, to represent the spatial orientation of objects in an image. Two approaches, Apriori-based and pattern-growth approaches, are proposed for mining co-orientation patterns. An experimental evaluation with synthetic datasets shows the advantage and disadvantage between these two algorithms.