Abstract
A novel shape description method, statistical chord-length features (SCLF), is proposed for shape retrieval. SCLF first describes the contour of a 2D shape using k/2 one-dimensional chord-length functions derived from partitioning the contour into k arcs of the same length, where k is the parameter of SCLF. The means and variances of all the chord-length functions are then calculated and a k dimensional feature vector is generated as a shape descriptor. Two experiments are conducted and the results show that SCLF achieves higher retrieval performance than traditional description methods such as geometric moment invariants and Fourier descriptors.
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Shi, C., Wang, B. (2006). Shape Retrieval Using Statistical Chord-Length Features. In: Chang, LW., Lie, WN. (eds) Advances in Image and Video Technology. PSIVT 2006. Lecture Notes in Computer Science, vol 4319. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949534_40
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DOI: https://doi.org/10.1007/11949534_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68297-4
Online ISBN: 978-3-540-68298-1
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