Research On Shape Feature Representation And Clustering Algorithm

Shape recognition and clustering are the key point in artificial intelligence, computer vision and pattern recognition. One of the most commonly encountered problems in shape clustering is how to select the feature without any given information. In recent years, dynamic programming, spectral graph theory, medial axis transform and data dimensionality reduction have received a lot of attention.This dissertation mainly discusses feature extraction, shape recognition and clustering. The main and pioneering works of this paper are as follows:1. An important task in pattern recognition is to cluster the given shapes. The proposed algorithm focuses on the shape recognition and retrieval. The algorithm first extracts the skeletal features using the medial axis transform. Then, the features are transformed into a string of symbols with the similarity among those symbols computed based on the edit distance. Finally, the shapes are identified using dynamic programming. Two public data sets are applied to verifying that the present approach is better than the compared approaches.2. Spectral graph theory has been exploited widely for the purpose of shape representation, matching and clustering. The proposed algorithm focuses on two problems:Firstly, we use skeletal feature points and spectrum to build up the mathematic model of the shape. Secondly, we embed shapes into a low dimensional space by analyzing the mathematic characteristic of the given models. Meanwhile, the simulation of clustering is achieved by studying the distribution of shapes in this space. Experiments on the public data sets illustrate that the presented algorithm is feasible for shape clustering.3. The spectrum of a graph has been widely used to characterize the properties of a graph and extract information from its structure. In this paper, we investigate the performance of Laplacian spectrum and multidimensional scaling (MDS) as shape recognition and clustering. Firstly, we extract boundary points to characterize the shape and to construct the Laplacian matrix. Secondly, the structural information of graph is described by using the eigenvalues of the Laplacian matrix. Finally, the given shapes are projected onto a low-dimensional space by performing MDS. Meanwhile, the clustering is achieved via analyzing the distribution of shapes. Comparative experiments on the public data sets demonstrate the validation of the proposed algorithm.4. Shape recognition and clustering are the key point in artificial intelligence and pattern recognition. By utilizing singular value decomposition (SVD) of Laplacian matrix and random walk model, mathematical model of shape structural feature is constructed. Based on this model and the public data sets, shape clustering is achieved by using state-vector. Moreover, the experimental results illustrate that the performance of proposed approach is better than that of other compared algorithms.