Perceptually Motivated Shape Context Which Uses Shape Interiors

TL;DR

This paper introduces a perceptually motivated shape context descriptor that incorporates interior shape properties, enhancing shape matching accuracy and retrieval performance.

Contribution

The paper proposes a novel shape descriptor that includes interior shape information, addressing limitations of contour-based methods in shape matching.

Findings

Improved shape retrieval rates with the new descriptor.

The interior properties significantly enhance matching accuracy.

The method is compatible with existing shape matching algorithms.

Abstract

In this paper, we identify some of the limitations of current-day shape matching techniques. We provide examples of how contour-based shape matching techniques cannot provide a good match for certain visually similar shapes. To overcome this limitation, we propose a perceptually motivated variant of the well-known shape context descriptor. We identify that the interior properties of the shape play an important role in object recognition and develop a descriptor that captures these interior properties. We show that our method can easily be augmented with any other shape matching algorithm. We also show from our experiments that the use of our descriptor can significantly improve the retrieval rates.