Contour and skeleton are two main stream representations for shape recognition in the literature. It has been shown that such two representations convey complementary information, however combining them in a nature way is nontrivial, as they are generally abstracted by different structures (closed string vs graph), respectively. This paper aims at addressing the shape recognition problem by combining contour and skeleton into a mid-level of shape representation. To form a mid-level representation for shape contours, a recent work named Bag of Contour Fragments (BCF) is adopted; While for skeleton, a new mid-level representation named Bag of Skeleton Paths (BSP) is proposed, which is formed by pooling the skeleton codes by encoding the skeleton paths connecting pairs of end points in the skeleton. Finally, a compact shape feature vector is formed by concatenating BCF with BSP and fed into a linear SVM classifier to recognize the shape. Although such a concatenation is simple, the SVM classifier can automatically learn the weights of contour and skeleton features to offer discriminative power. The encouraging experimental results demonstrate that the proposed new shape representation is effective for shape classification and achieves the state-of-the-art performances on several standard shape benchmarks.
@cite_14: Shape analysis has been a long standing problem in the literature. In this paper, we address the shape classification problem and make the following contributions: (1) We combine both contour and skeleton (also local and global) information for shape analysis, and we derive an effective classifier. (2) We collect a challenging shape database in which there are 20 categories of animals, with each having 100 shapes. All these shapes are obtained from real images with a large variation in pose, viewing angle, articulation, and self-occlusion. (3) We emphasize the importance of having good representation for shape classification to address the unique characteristics of shape. A thorough experimental study is conducted showing significant improvement by the proposed algorithm over many of the state-of-the-art shape matching and classification algorithms, on both our dataset and the well-known MPEG-7 dataset [19]. In addition, we applied our algorithm for recognizing and classifying objects from natural images and obtained very encouraging results.
@cite_15: Both example-based and model-based 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. This paper proposes a part-based approach to address this problem. Bayesian classification is performed within a three-level framework, which consists of models for contour segments, for classes, and for the entire database of training examples. The class model enables different parts of different exemplars of a class to contribute to the recognition of an input shape. The method is robust to occlusion and is invariant to planar rotation, translation, and scaling. Furthermore, the method is completely automated. It achieves 98 classification accuracy on a large database with many classes.
@cite_45: In this paper, we proposed a novel approach to shape classification. A new shape tree based on junction nodes can represent the global structure in a simple way. The statistic distribution of junctions can be learned by merging the shape trees. In the process of learning, context of a junction node is obtained to improve the rate of classification. We illustrate the utility of the proposed method on the problem of 2D shape classification using the new shape tree representation.
Generative models are also used for shape recognition. Sun and Super @cite_15 propose a Bayesian model, which use the normalized contour fragments as the input features for shape classification. Wang @cite_45 model shapes of one class by a skelet al prototype tree learned by skeleton graph matching. Then a Bayesian inference is used to compute the similarity between a testing skeleton and each skelet al prototype tree. Bai @cite_14 propose to integrate contour and skeleton by a Gaussian mixture model, in which contour fragments and skeleton paths are used as the input features. Unlike their method, ours encodes the contour and skeleton features into one shape descriptor according to the association between contour and skeleton. Therefore, we avoid the intractable step to finetune the weight between contour and skeleton models.