Ensemble of shape descriptors for shape retrieval and classification Online publication date: Sat, 28-Jun-2014
by Loris Nanni; Alessandra Lumini; Sheryl Brahnam
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 6, No. 2, 2014
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.
Online publication date: Sat, 28-Jun-2014
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Advanced Intelligence Paradigms (IJAIP):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com