An unification of Inner Distance Shape Context and Local Binary Pattern for Shape Representation and Classification

Abstract

In this paper, we propose a combined classifier approach based on Inner Distance Shape Context (IDSC) and Local Binary Pattern (LBP) to classify shapes accurately. The inner-distance is insensitive to shape articulations and the LBP is invariant to rotation and shift of the shape. The Dynamic Programming (DP) in case of IDSC and Earth Movers Distance (EMD) metric in case of LBP were respectively employed to obtain similarity and hence used to classify given query shape based on maximum similarity value. The experiments are conducted on publicly available shape datasets namely MPEG-7, Kimia-99, Kimia-216, Myth and Tools-2D and the results are presented by means of Bulls eye score and precision-recall metric. The comparative study is also provided with the well known approaches to determine the retrieval accuracy of the proposed approach. The experimental results demonstrate that the proposed approach yield significant improvements over baseline shape matching algorithms.