Robust symbolic representation for shape recognition and retrieval
Pattern Recognition
Robust symbolic representation for shape recognition and retrieval
Pattern Recognition
A string matching approach for visual retrieval and classification
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Trajectory representation using Gabor features for motion-based video retrieval
Pattern Recognition Letters
Classification of silhouettes using contour fragments
Computer Vision and Image Understanding
Shape recognition based on Kernel-edit distance
Computer Vision and Image Understanding
Shape classification by manifold learning in multiple observation spaces
Information Sciences: an International Journal
Hi-index | 0.00 |
In this paper we present two algorithms for shape recognition. Both algorithms map the contour of the shape to be recognized into a string of symbols. The first algorithm is based on supervised learning using string kernels as often used for text categorization and classification. The second algorithm is very weakly supervised and is based on the Procrustes analysis and on the Edit distance used for computing the similarity between strings of symbols. The second algorithm correctly recognizes 98.29 % of shapes from the MPEG-7 database, i.e. better than any previous algorithms. The second algorithm is able also to retrieve similar shapes from a database.