An introduction to spatial database systems
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
Introduction to Combinatorial Pyramids
Digital and Image Geometry, Advanced Lectures [based on a winter school held at Dagstuhl Castle, Germany in December 2000]
Information Processing and Management: an International Journal
Composing cardinal direction relations
Artificial Intelligence
IEEE Transactions on Knowledge and Data Engineering
Contains and inside relationships within combinatorial pyramids
Pattern Recognition
Classifying Images from Athletics Based on Spatial Relations
SMAP '07 Proceedings of the Second International Workshop on Semantic Media Adaptation and Personalization
Qualitative spatial relationships for image interpretation by using semantic graph
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Markov random fields and spatial information to improve automatic image annotation
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
A multiple substructure matching algorithm for fingerprint verification
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Spatial template extraction for image retrieval by region matching
IEEE Transactions on Image Processing
Frequent approximate subgraphs as features for graph-based image classification
Knowledge-Based Systems
Multimedia Tools and Applications
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It has been proved that spatial relations among objects and object's parts play a fundamental role in the human perception and understanding of images, thus becoming very relevant in the computational fields of object recognition and content-based image retrieval. In this work we propose a spatial descriptor to represent topological and orientation/directional relationships, which are obtained by means of combinatorial pyramids. A combination of visual and spatial features is performed to improve the object recognition task. We ran an experiment to evaluate the expressiveness of this representation and it has shown promising results. It was performed on the benchmark ETH-80 Image Set database and we compare our approach with a state-of-the-art method recently published.