In this paper, we present a decision level fused local Morphological Pattern Spectrum (PS) and Local Binary Pattern (LBP) approach for an efficient shape representation and classification. This method makes use of Earth Movers Distance (EMD) as the measure in feature matching and shape retrieval process. The proposed approach has three major phases: Feature Extraction, Construction of hybrid spectrum knowledge base and Classification. In the first phase, feature extraction of the shape is done using pattern spectrum and local binary pattern method. In the second phase, the histograms of both pattern spectrum and local binary pattern are fused and stored in the knowledge base. In the third phase, the comparison and matching of the features, which are represented in the form of histograms, is done using Earth Movers Distance (EMD) as metric. The top-n shapes are retrieved for each query shape. The accuracy is tested by means of standard Bulls eye score method. The experiments are conducted on publicly available shape datasets like Kimia-99, Kimia-216 and MPEG-7. The comparative study is also provided with the well known approaches to exhibit the retrieval accuracy of the proposed approach.
The techniques which involve graph matching by finding the correspondence between nodes through the conversion of skeleton graphs to skeleton trees require heuristic rules to select the root node @cite_19 @cite_17 . The major drawback of this method is that a small change in the shape causes the root to change that results in significant change in the topology of the tree representation. Apart from this, the conversion from graph to a tree structure results in loss of significant structural information and hence leads to wrong match @cite_24 . In 2008, Bai and Latecki @cite_24 proposed a method based on the path similarity between the end points of the skeleton graphs. In this method, the geodesic paths between skeleton end points are obtained and are matched using Optimal Subsequence Bijection (OSB) method. Unlike other methods, this method does not convert the skeleton into a graph and matching the graphs,which is still an open problem. This approach addresses the problem of shapes having similar skeleton graphs, but different topological structures and having different skeletons with visually similar shapes. This approach works well even in the presence of articulation and contour deformation.
@cite_24 @cite_19 @cite_17
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This paper proposes a novel graph matching algorithm and applies it to shape recognition based on object silhouettes. The main idea is to match skeleton graphs by comparing the geodesic paths between skeleton endpoints. In contrast to typical tree or graph matching methods, we do not consider the topological graph structure. Our approach is motivated by the fact that visually similar skeleton graphs may have completely different topological structures. The proposed comparison of geodesic paths between endpoints of skeleton graphs yields correct matching results in such cases. The skeletons are pruned by contour partitioning with discrete. Curve evolution, which implies that the endpoints of skeleton branches correspond to visual parts of the objects. The experimental results demonstrate that our method is able to produce correct results in the presence of articulations, stretching, and contour deformations. In this paper, we propose a general framework for graph matching which is suitable for different problems of pattern recognition. The pattern representation we assume is at the same time highly structured, like for classic syntactic and structural approaches, and of subsymbolic nature with real-valued features, like for connectionist and statistic approaches. We show that random walk based models, inspired by Google's PageRank, give rise to a spectral theory that nicely enhances the graph topological features at node level. As a straightforward consequence, we derive a polynomial algorithm for the classic graph isomorphism problem, under the restriction of dealing with Markovian spectrally distinguishable graphs (MSD), a class of graphs that does not seem to be easily reducible to others proposed in the literature. The experimental results that we found on different test-beds of the TC-15 graph database show that the defined MSD class "almost always" covers the database, and that the proposed algorithm is significantly more efficient than top scoring VF algorithm on the same data. Most interestingly, the proposed approach is very well-suited for dealing with partial and approximate graph matching problems, derived for instance from image retrieval tasks. We consider the objects of the COIL-100 visual collection and provide a graph-based representation, whose node's labels contain appropriate visual features. We show that the adoption of classic bipartite graph matching algorithms offers a straightforward generalization of the algorithm given for graph isomorphism and, finally, we report very promising experimental results on the COIL-100 visual collection. It is well-known that the problem of matching two relational structures can be posed as an equivalent problem of finding a maximal clique in a (derived) "association graph." However, it is not clear how to apply this approach to computer vision problems where the graphs are hierarchically organized, i.e., are trees, since maximal cliques are not constrained to preserve the partial order. We provide a solution to the problem of matching two trees by constructing the association graph using the graph-theoretic concept of connectivity. We prove that, in the new formulation, there is a one-to-one correspondence between maximal cliques and maximal subtree isomorphisms. This allows us to cast the tree matching problem as an indefinite quadratic program using the Motzkin-Straus theorem, and we use "replicator" dynamical systems developed in theoretical biology to solve it. Such continuous solutions to discrete problems are attractive because they can motivate analog and biological implementations. The framework is also extended to the matching of attributed trees by using weighted association graphs. We illustrate the power of the approach by matching articulated and deformed shapes described by shock trees. We introduce a novel 3D tree skeletonization method from multiple images.Affine transformation and iterative tracking are added to PyrLK optical flow.Parallel voxel flooding is proposed for determining point cloud of single branch.L-System extraction algorithm for tree skeletons is proposed. Display Omitted 3D tree models are widely applied to construct large-scale virtual scenes. However, converting real trees into computer representation faces two main problems. One is the low quality of reconstructed point cloud. The other is that the skeletonization produces inaccurate results due to the complex structure of trees. We propose a novel pipeline to reconstruct automatically accurate 3D tree skeletons from sequential tree images. It involves three steps: (1) Pre-processing: an optical flow based feature-matching algorithm is proposed to acquire high-quality point clouds. (2) Shape-based parallel skeletonization: tree shape is taken into consideration to detect specific structure features of trees during parallel flooding. (3) Post-processing: an L-System extraction algorithm is applied on the skeletons and the lightweight representation is obtained to meet various requirements. The effectiveness of our approach is demonstrated through experiments.