Authors: Loris Nanni; Alessandra Lumini; Sheryl Brahnam
Addresses: Department of Information Engineering, University of Padua, Via Gradenigo 6/A, 35131 – Padova, Italy ' DISI, University of Bologna, via Venezia 52, 47023 Cesena, Italy ' Missouri State University, 901 S. National, Springfield, MO 65804, USA
Abstract: Shape classification has long been a field of study in computer vision. In this work, we propose an ensemble of approaches using the weighted sum rule that is based on a set of widely used shape descriptors (inner-distance shape context, shape context, and height functions). Features are obtained by transforming these shape descriptors into a matrix from which a set of texture descriptors are extracted. The different descriptors are then compared using the Jeffrey distance. We validate our ensemble on seven widely used datasets (MPEG7 CE-Shape-1, Kimia silhouettes, Tari dataset, a leaf dataset, a tools dataset, a myths figures dataset, and motif pottery dataset), where the parameters of each method and the weights of the weighted fusion are kept the same across all seven datasets, thereby producing a general-purpose shape classification system. Our experimental results demonstrate that our new generalised approach offers significant improvements over baseline shape matching algorithms.
Keywords: shape classification; ensemble; weighted sum rule; Jeffrey distance; texture descriptors; shape descriptors; shape retrieval; computer vision; shape matching.
International Journal of Advanced Intelligence Paradigms, 2014 Vol.6 No.2, pp.136 - 156
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 01 Jun 2014 *