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access icon openaccess Shape retrieval by using multi-scale angle-based representation and dynamic label propagation

This is an open access article published by the IET and Zhejiang University Press under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 14/08/2020, Accepted 06/10/2020, Revised 28/09/2020, Published 27/10/2020

To improve the robustness and discrimination power of the triangle-area representation, a novel shape matching method based on multi-scale angle representation is proposed in this study. By analysing the configurations of different sample points from each shape contour, shape descriptors are constructed by using space angles at different scale levels. With the proposed shape representation, the multi-scale information of shape contours is efficiently described, and the dynamic programming is further used to determine the correspondence between samples from different shapes and calculate the shape distance in the feature matching step. Moreover, to improve the shape retrieval results based on pairwise shape distances, the dynamic label propagation is introduced as the post-processing step. Unlike previous distance learning methods learning the database manifold implicitly, the authors method retrieves relative objects on the shortest paths from near to far explicitly, and the underlying structure can be effectively captured. The proposed method tested on different shape databases provides the performances superior to many other methods, and it can be applied to visual data processing and understanding of the internet of things.

Inspec keywords: image retrieval; dynamic programming; feature extraction; shape recognition; image representation; image matching; distance learning; learning (artificial intelligence)

Other keywords: different scale levels; multiscale information; pairwise shape distances; space angles; discrimination power; dynamic label propagation; multiscale angle-based representation; shape distance; feature matching step; triangle-area representation; different shape databases; multiscale angle representation; dynamic programming; authors method; shape matching method; previous distance learning methods; shape representation; shape retrieval results; shape descriptors; robustness; shape contour; different sample points

Subjects: Computer-aided instruction; Image recognition; Information retrieval techniques; Computer vision and image processing techniques; Knowledge engineering techniques; Optimisation techniques

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