Shape Representation and Classification Based on Discrete Cosine Transformation and IDSC

In this paper, we propose a combined classifier model based on two dimensional discrete cosine transform (2D-DCT) and Inner Distance Shape Context (IDSC) to classify shapes accurately. DCT is capable of capturing the region information and the inner-distance is insensitive to shape articulations. We propose to integrate these two techniques for accurate shape classification. The Euclidean distance metric in case of DCT and Dynamic Programming (DP) in case of IDSC 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 accuracy of the proposed approach. The experimental results demonstrate that the proposed approach yields significant improvements over baseline shape matching algorithms.