Abstract:  Shape analysis has been one of the most studied topics in computer vision. One major task in shape analysis is to study the underlying statistics of shape population and use the information to extract , recognize , and
understand physical structures and biological objects. Matching based algorithms perform classification , essentially through exemplar based or nearest neighborhood approach by matching the query shape against all those in the
training set. On few training samples , these algorithms are hard to capture the large intra - class variation. On largetraining samples , it is extremely time consuming to perform shape matching one - by - one. Approaches based on
generative models require a large number of parameters , which renders them significantly more expensive computationally , and also increases the possibility of converging to non - optimal local minima. Furthermore , existing
Matching based and model - based approaches cannot handle object classes that have different parts or numbers of parts without splitting the class into separate subclasses. Most of the methods for shape classification are based on
contour and many researchers have worked on the general shape classification problem. However , approaches for classifying contour shapes can encounter difficulties when dealing with classes that have large nonlinear variability ,
especially when the variability is structural or due to articulation. A novel method , using contour critical point sets (CCPS) to perform shape classification task , is proposed in this paper. First , inner - distance shape context (IDSC)
is used to characterize the critical points. Of course , other features of the critical points may instead of IDSC. Shapes are represented by a set of points sampled from the shape contours and the shape context at a reference point
captures the distribution of the remaining points relative to it , thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts. The inner -distance is
defined as the length of the shortest path between landmark points within the shape silhouette. It is articulation insensitive and more effective at capturing part structures than the Euclidean distance. This suggests that the inner -
distance can be used as a replacement for the Euclidean distance to build more accurate descriptors for complex shapes , especially for those with articulated parts. Humans perception of shape is based on similarity of common
parts , to the extent that a single , significant visual part is sufficient to recognize the whole object and part -based representations allow for recognition that is robust in the presence of occlusion , movement , deletion , or growth of
portions of an object. It is a simple and natural observation that maximal convex or concave parts of objects determine visual parts. So the contour critical point sets (CCPS) of shapes is utilized to perform shape classification
task. The IDSC of critical point is an excellent feature of contour point , which not only contains local features but also the global information. After design the smart feature of shapes , then , Bayesian classification is performed
within a three - level framework which consists of models for contour critical point sets , for classes , and for the entire database of training examples. The class model enables different critical points of different exemplars of one class to
contribute to the recognition of an input shape. This new method achieves 100 % classification accuracy on Kimia database. Furthermore , to classify all 108 test shapes only need 8 seconds , which is the best performance ever
reported in the literature. The results on the well 2 known MPEG7 CE 2 Shape -1 data set also prove its superiority.