Fourier Transform to Group Feature on Generated Coarser Contours for Fast 2D Shape Matching

Abstract

Fourier descriptors are classical global shape descriptors with high matching speed but low accuracy. To obtain higher accuracy, a novel framework for forming Fourier descriptors is proposed and named as MSFDGF (multiscale Fourier descriptor using group feature). MSFDGF achieves multiscale description by generating coarser contours. Then, a group of complementary features are extracted on the generated coarser contours. Finally, Fourier transform is performed on the features. MSFDGF-SH is a new global descriptor using the MSFDGF framework and shape histograms. Experiments are conducted on four databases, which are MPEG-7 CE-1 Part B, Swedish Plant Leaf, Kimia 99 and Expanded Articulated Database, to evaluate the performance of MSFDGF-SH. The experimental results show that MSFDGF-SH is an effective and efficient global shape descriptor. This new descriptor has a high accuracy of 87.76%, which exceeds the Shape Tree on the MPEG-7 CE-1 Part B dataset. This is the first Fourier descriptor that surpasses the Shape Tree method in terms of both accuracy and speed on this dataset.

Extraction of compact boundary normalisation based geometric descriptors for affine invariant shape retrieval

“…Further shape descriptors with affine invariance support is focused by many authors [24,[27][28][29][30][31]. Recently, owing to the availability of huge computational resources, many authors have used the feature fusion and multi-scale representation of shapes, which consequently increase the overall performances [2,10,17,[32][33][34][35][36]. The multi-resolution rotation, scaling, translation (RST) invariant shape descriptor designed by Attalla and Siy is based on polygon approximation [20].…”

“…Recently, Zheng et al. have extracted shape histogram based multi‐scale Fourier descriptor using group feature (MSFDGF‐SH) from courser contour of the shapes for 2D shape matching applications [36].…”

“…In recent reports, the feature fusion and multi-scale shape description methods have widely been used as they capture the maximum information of shapes [2,4,10,17,[32][33][34][35][36]]. An affine invariant feature fusion method, introduced by Yu et al, performs the parallel extraction of geometric features with a twostage double biologically inspired transformation (DBIT) features from the images [17].…”

“…Further shape descriptors with affine invariance support is focused by many authors [24,[27][28][29][30][31]. Recently, owing to the availability of huge computational resources, many authors have used the feature fusion and multi-scale representation of shapes, which consequently increase the overall performances [2,10,17,[32][33][34][35][36].…”

“…Hu et al have developed a common base triangle area (CBTA) descriptor which uses series of base-fixed and vertex-varied triangles to represent shape objects [2]. Recently, Zheng et al have extracted shape histogram based multi-scale Fourier descriptor using group feature (MSFDGF-SH) from courser contour of the shapes for 2D shape matching applications [36].…”