Automatic hierarchical classification of silhouettes of 3D objects

This work proposes a curve dissimilarity measure which relies upon a novel curve matching syntactic algorithm, and uses it to represent the database as a complete graph, with nodes representing the images and Dissimilarity values assigning weights to the edges.

Abstract

The organization of image databases can rely upon different aspects of image similarity. Here we extract silhouettes from images of three dimensional objects, and rely upon curve similarity for image classification. Our scheme avoids the embedding of images in a vector space. Instead, we propose a curve dissimilarity measure which relies upon a novel curve matching syntactic algorithm, and use it to represent the database as a complete graph, with nodes representing the images and dissimilarity values assigning weights to the edges. A robust clustering algorithm, which is based on a physical ferromagnet model, is used to find the hierarchical structure underlying the collection of images. We tested our scheme with a database of 90 real images of 6 objects, some of them very different, others rather similar. We get a perfect hierarchical classification of these images into 6 classes of objects belonging to 3 different families.