Shape representation and description using the Hilbert curve

In this paper, a novel linear-time approach to shape representation and description is presented. The object shape is captured by scanning the object image using a space-filling curve (SFC). The resulting vector is smoothed, using wavelet approximation, and sampled. In addition, the concept of key feature points (KFPs) is introduced to utilize a priori information about the classification of the images in the database in optimizing the representation of the objects within each class. The proposed technique achieves a recognition rate of 88.3% on the MPEG-7 core experiment part B. On the Kimia-99 and Kimia-216 datasets, a precision average of 95.6% is attained. Retrieval rates of 94.2% and 95.6% are achieved on the gray-scale and binary versions of the ETH-80 dataset. respectively. (c) 2008 Elsevier B.V. All rights reserved.