Partial similarity of objects and text sequences

Similarity is one of the most important abstract concepts in the human perception of the world. In computer vision, numerous applications deal with comparing objects observed in a scene with some a priori known patterns. Often, it happens that while two objects are not similar, they have large similar parts, that is, they are partially similar. Here, we present a novel approach to quantify this semantic definition of partial similarity using the notion of Pareto optimality. We exemplify our approach on the problems of recognizing non-rigid objects and analyzing text sequences.