Discrete Cosine Transformation and Height Functions Based Shape Representation and Classification

In this paper, we propose a combined classifier model based on two dimensional discrete cosine transform (2D-DCT) and Height Functions (HF) for accurate shape representation and classification. The DCT is capable of capturing the region information and Height Functions are insensitive to geometric transformations and nonlinear deformations. The Euclidean distance metric in case of DCT and Dynamic Programming (DP) in case of HF were respectively employed to obtain similarity values and hence fused to classify 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 and precision-recall metric. The comparative study is also provided with the well known approaches to exhibit the retrieval accu- racy of the proposed approach. The experimental results demonstrate that the proposed approach yields significant improvement over some of the well known algorithms.