Recent literature comprises a large number of papers on the query and retrieval of visual information based on its content. At the same time, a number of prototype systems have been implemented enabling searching through on-line image databases and still image retrieval. However, it has been often pointed out that meaningful/semantic information should be extracted from visual information in order to improve the efficiency and functionality of a content-based retrieval tool. In this context, present work focuses on the extraction of objects from images and video clips and modeling of the resulting object contours using B-splines. Affine-invariant curve representation is obtained through Normalized Fourier descriptors (NFD), curve moments, as well as a novel curve normalization algorithm that leads to major preservation of object shape information. A neural network approach is employed for supervised classification of video objects into prototype object classes. Experiments on several real-life and simulated video sequences are included to evaluate the classification results for all affine-invariant representations used.