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]
Contraction kernels and combinatorial maps
Pattern Recognition Letters - Special issue: Graph-based representations in pattern recognition
Information Processing and Management: an International Journal
Composing cardinal direction relations
Artificial Intelligence
Vision pyramids that do not grow too high
Pattern Recognition Letters - Special issue: In memoriam Azriel Rosenfeld
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
IEEE Transactions on Knowledge and Data Engineering
Tensor Discriminant Analysis for View-based Object Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Character Recognition Systems: A Guide for Students and Practitioners
Character Recognition Systems: A Guide for Students and Practitioners
Contains and inside relationships within combinatorial pyramids
Pattern Recognition
International Journal of Computer Vision
The Pyramid Match Kernel: Efficient Learning with Sets of Features
The Journal of Machine Learning Research
Classifying Images from Athletics Based on Spatial Relations
SMAP '07 Proceedings of the Second International Workshop on Semantic Media Adaptation and Personalization
Adaptive image retrieval based on the spatial organization of colors
Computer Vision and Image Understanding
Description of interest regions with local binary patterns
Pattern Recognition
Learning Object Representations Using Sequential Patterns
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
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
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding semantic structures in image hierarchies using Laplacian graph energy
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Assessing the role of spatial relations for the object recognition task
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Block-Based methods for image retrieval using local binary patterns
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Spatial template extraction for image retrieval by region matching
IEEE Transactions on Image Processing
Visual graph modeling for scene recognition and mobile robot localization
Multimedia Tools and Applications
Segmentation-based multi-class semantic object detection
Multimedia Tools and Applications
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Spatial relations among objects and object parts play a fundamental role in the human perception and understanding of images, thus becoming very relevant in the computational fields of object recognition, scene understanding and content-based image retrieval. In this work we propose a graph matching scheme that involves color, texture and shape features along with spatial descriptors to represent topological and orientation/directional relationships--which are obtained by means of combinatorial pyramids--in order to identify similar objects from a database. We also suggest a method for deciding which are the more useful levels in the hierarchy of segmentation for the recognition process. Our main objective is to prove that the combination of visual and spatial features is a promising road in order to improve the object recognition task. We performed experiments on two well known databases, COIL-100 and ETH-80 image sets, in order to evaluate the expressiveness of the proposed representation. These sets introduce challenges for simple object recognition in terms of view-point changes, and our results were comparable or superior than other state-of-the-art methods.