Matching Experiments using Shape Context

We perform experiments matching the SIFT points and the extrema points extracted using our LB algorithm. We use the shape context approach proposed by Belongie et al.[1] and the software available at his website to compute the descriptors based on the extracted points. The matching algorithm uses the approach proposed by Ofir Pele and Michael Werman [2]. The code is available at the author's webpage.

Matching LB points

The first experiment matches the 2 point sets obtained using our approach:

We observe that the points in the second image are mostly located on the periphery.

Matching SIFT points

Since the shape context approach considers the points as a unity, i.e., the points are related to each other in a certain way. That structure should be preserved from one image to the other.

The following example uses the extracted points using our approach in the first image and the SIFT points in the second. Coincidently the structure of the set points are similar.

Matching LB points with SIFT points

We need to explore this situation and to evaluate if the shape context approach with the extracted points seen as sets of points are suitable for the matching step.

[1] G. Mori, S. Belongie, and J. Malik. "Shape Contexts Enable Efficient Retrieval of Similar Shapes".CVPR 2001

[2] Ofir Pele and Michael Werman. "The Quadratic-Chi Histogram Distance Family". ECCV 2010.