SIAM Journal on Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape measures for content based image retrieval: a comparison
Information Processing and Management: an International Journal
Computational geometry in C (2nd ed.)
Computational geometry in C (2nd ed.)
Determining the minimum-area encasing rectangle for an arbitrary closed curve
Communications of the ACM
Experimental evaluation of an on-line scribble recognizer
Pattern Recognition Letters
Resiliency and Robustness of Alternative Shape-Based Image Retrieval
IDEAS '00 Proceedings of the 2000 International Symposium on Database Engineering & Applications
Machine Vision: Theory, Algorithms, Practicalities
Machine Vision: Theory, Algorithms, Practicalities
Towards 3D modeling using sketches and retrieval
SBM'04 Proceedings of the First Eurographics conference on Sketch-Based Interfaces and Modeling
Geometric matching for clip-art drawing retrieval
Journal of Visual Communication and Image Representation
An Approach to the Parameterization of Structure for Fast Categorization
International Journal of Computer Vision
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We present a shape classification technique for structural content–based retrieval of two-dimensional vector drawings. Our method has two distinguishing features. For one, it relies on explicit hierarchical descriptions of drawing structure by means of spatial relationships and shape characterization. However, unlike other approaches which attempt rigid shape classification, our method relies on estimating the likeness of a given shape to a restricted set of simple forms. It yields for a given shape, a feature vector describing its geometric properties, which is invariant to scale, rotation and translation. This provides the advantage of being able to characterize arbitrary two–dimensional shapes with few restrictions. Moreover, our technique seemingly works well when compared to established methods for two dimensional shapes.