Shape similarity measure based on correspondence of visual parts

TLDR

The paper proposes a cognitively motivated similarity measure for retrieving similar 2D silhouette objects from image databases. The method simplifies shapes using digital curve evolution, establishes optimal visual‑part correspondences without explicit part extraction, aggregates part similarities, and is evaluated against existing shape‑matching techniques. Experiments show the procedure yields intuitive correspondences and remains robust to noise.

Abstract

A cognitively motivated similarity measure is presented and its properties are analyzed with respect to retrieval of similar objects in image databases of silhouettes of 2D objects. To reduce influence of digitization noise, as well as segmentation errors, the shapes are simplified by a novel process of digital curve evolution. To compute our similarity measure, we first establish the best possible correspondence of visual parts (without explicitly computing the visual parts). Then, the similarity between corresponding parts is computed and aggregated. We applied our similarity measure to shape matching of object contours in various image databases and compared it to well-known approaches in the literature. The experimental results justify that our shape matching procedure gives an intuitive shape correspondence and is stable with respect to noise distortions.