Ensemble of shape descriptors for shape retrieval and classification
Loris Nanni, Alessandra Lumini, Sheryl Brahnam · International Journal of Advanced Intelligence Paradigms · 2014
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.