An algorithm for finding nearest neighbours in (approximately) constant average time
Pattern Recognition Letters
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
BAS: a perceptual shape descriptor based on the beam angle statistics
Pattern Recognition Letters
Symmetry-Based Indexing of Image Databases
CBAIVL '98 Proceedings of the IEEE Workshop on Content - Based Access of Image and Video Libraries
Exact indexing of dynamic time warping
Knowledge and Information Systems
Shape Classification Using the Inner-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape retrieval using triangle-area representation and dynamic space warping
Pattern Recognition
Faster retrieval with a two-pass dynamic-time-warping lower bound
Pattern Recognition
The VLDB Journal — The International Journal on Very Large Data Bases
Articulation-invariant representation of non-planar shapes
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Shape matching and classification using height functions
Pattern Recognition Letters
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
MPEG-7 visual shape descriptors
IEEE Transactions on Circuits and Systems for Video Technology
A multiscale representation method for nonrigid shapes with a single closed contour
IEEE Transactions on Circuits and Systems for Video Technology
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Cyclic Dynamic Time Warping (CDTW) is a good dissimilarity of shape descriptors of high dimensionality based on contours, but it is computationally expensive. For this reason, to perform recognition tasks, a method to reduce the number of comparisons and avoid an exhaustive search is convenient. The Approximate and Eliminate Search Algorithm (AESA) is a relevant indexing method because of its drastic reduction of comparisons, however, this algorithm requires a metric distance and that is not the case of CDTW. In this paper, we introduce a heuristic based on the intrinsic dimensionality that allows to use CDTW and AESA together in classification and retrieval tasks over these shape descriptors. Experimental results show that, for descriptors of high dimensionality, our proposal is optimal in practice and significantly outperforms an exhaustive search, which is the only alternative for them and CDTW in these tasks.