Title :
Affine invariant shape descriptors: The ICA-Fourier descriptor and the PCA-Fourier descriptor
Author :
Mei, Ye ; Androutsos, Dimitrios
Author_Institution :
Dept. of Electr. & Comput. Eng., Ryerson Univ., Toronto, ON
Abstract :
In this paper, we propose two new affine invariant shape descriptors, the ICA-Fourier descriptor and the PCA-Fourier descriptor. We tested the descriptors by using them as features for shape based silhouette image retrieval. Experiments on a 1000 silhouette image database show promising retrieval rates of 95.41% and 93.63%, using the ICA-Fourier descriptor and the PCA-Fourier descriptor, respectively. The relationship between those two descriptors are also explained. The proposed PCA-Fourier descriptor is computationally more efficient than its ICA counterpart, while having comparable performance.
Keywords :
Fourier transforms; affine transforms; feature extraction; image retrieval; independent component analysis; principal component analysis; ICA-Fourier descriptor; PCA-Fourier descriptor; affine invariant shape descriptor; image database; independent component analysis; principal component analysis; shape based silhouette image retrieval; Computer vision; Fourier transforms; Image databases; Image retrieval; Independent component analysis; Information retrieval; Object recognition; Shape; Testing; Vectors;
Conference_Titel :
Pattern Recognition, 2008. ICPR 2008. 19th International Conference on
Conference_Location :
Tampa, FL
Print_ISBN :
978-1-4244-2174-9
Electronic_ISBN :
1051-4651
DOI :
10.1109/ICPR.2008.4761381