Title

Ensemble of shape descriptors for shape retrieval and classification

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.

Department(s)

Information Technology and Cybersecurity

Document Type

Article

DOI

https://doi.org/10.1504/IJAIP.2014.062177

Keywords

Keywordsshape classification, ensemble, weighted sum rule, Jeffrey distance, texture descriptors

Publication Date

2014

Journal Title

International Journal of Advanced Intelligence Paradigms

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