Iconic indexing by 2-D strings
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Sets and Systems - Special issue on fuzzy methods for computer vision and pattern recognition
Design and evaluation of algorithms for image retrieval by spatial similarity
ACM Transactions on Information Systems (TOIS)
Photobook: content-based manipulation of image databases
International Journal of Computer Vision
A New Way to Represent the Relative Position between Areal Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Indexing and retrieval of video based on spatial relation sequences
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 2)
Maintaining knowledge about temporal intervals
Communications of the ACM
Introduction to Algorithms
Picture Similarity Retrieval Using the 2D Projection Interval Representation
IEEE Transactions on Knowledge and Data Engineering
ImageMap: An Image Indexing Method Based on Spatial Similarity
IEEE Transactions on Knowledge and Data Engineering
Comparison of spatial relation definitions in computer vision
ISUMA '95 Proceedings of the 3rd International Symposium on Uncertainty Modelling and Analysis
R-Histogram: quantitative representation of spatial relations for similarity-based image retrieval
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Qualitative Spatial Representation and Reasoning: An Overview
Fundamenta Informaticae - Qualitative Spatial Reasoning
Context-Based Object-Class Recognition and Retrieval by Generalized Correlograms
IEEE Transactions on Pattern Analysis and Machine Intelligence
A graph-based fuzzy linguistic metadata schema for describing spatial relationships
Proceedings of the 2011 Visual Information Communication - International Symposium
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Representation of relative spatial relations between objects is often required in many multimedia database applications because spatial relations between objects in an image convey important information about the image. Quantitative representation of spatial relations taking into account shape, size, orientation and distance is often required. The R-Histogram is such a quantitative representation of spatial relations between two objects. However, this method only considers pixels on the object boundary, assuming that the objects are homeomorphic to a 2-ball. For objects with more complicated topology, we propose in this paper the R*-Histogram, a new extension to the R-Histogram. The R*-Histogram generalizes the R-Histogram by taking into account all the pixels in the objects. We also introduce an efficient O(kN log N) time algorithm to compute the R*-Histogram, which is asymptotically faster than the original O(N2) time algorithm for the R-Histogram even when k=O(n). Here, N=n2 denotes the number of pixels in the processed n x n image and k is the number of different directions considered. The effectiveness of the R*-Histogram is evaluated empirically with a Query By Example (QBE) system on a database of 2000 synthetic images containing objects with complicated shape and topology. Experiments have shown that the similarly search results match human intuition very well.