An ensemble of morphology based Pattern Spectrum and Height functions for shape representation and classification

In this paper, we propose a combined classifier model based on Pattern Spectrum (PS) and Height functions (HF) to classify shapes accurately. The PS captures the intrinsic details of shape, the HF is insensitive to geometric transformations and nonlinear deformations, and hence combined to represent/classify shapes accurately. The Earth Movers Distance (EMD) metric in case of PS and Dynamic Programming (DP) in case of HF were respectively employed to obtain similarity values and fused to classify a given query shape based on minimum 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 Bull´s eye score. The comparative study is also provided with the well known approaches to exhibit the retrieval accuracy of the proposed approach. The proposed approach shows better results compared to other algorithms.