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.
Skeletons can be organized in the form of attribute-relation graphs (ARG) for the matching purpose. One of the examples for ARG is shock graphs introduced by @cite_9 @cite_11 . Shock Graph is an abstraction of skeleton of a shape onto a Directed Acyclic Graph (DAG). The skeleton points are labeled according to the radius function at each point. Shock graphs are constructed using the specialized grammar called Shock Grammar @cite_10 . In the skeleton, the branch points, end-points, and skeleton segments contain both geometrical and topological information. These primitives are referred as shocks @cite_6 @cite_23 . The concept of bone graph is an extension of shock graph. Bone graphs retain the non-ligature structures of the shock graph and are more stable @cite_12 . There are several algorithms which are proposed in order to have an efficient match of these skelet al graphs. @cite_23 proposed a matching approach for node-attributed trees which measures the edit distance between two graphs. Since this method involves complex edit operation, it suffers from high computational complexity.
@cite_9 @cite_6 @cite_23 @cite_10 @cite_12 @cite_11
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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. We have been developing a theory for the generic representation of 2-D shape, where structural descriptions are derived from the shocks (singularities) of a curve evolution process, acting on bounding contours. We now apply the theory to the problem of shape matching. The shocks are organized into a directed, acyclic shock graph, and complexity is managed by attending to the most significant (central) shape components first. The space of all such graphs is highly structured and can be characterized by the rules of a shock graph grammar. The grammar permits a reduction of a shockgraph to a unique rooted shock tree. We introduce a novel tree matching algorithm which finds the best set of corresponding nodes between two shock trees in polynomial time. Using a diverse database of shapes, we demonstrate our system's performance under articulation, occlusion, and changes in viewpoint. Medial descriptions, such as shock graphs, have gained significant momentum in the shape-based object recognition community due to their invariance to translation, rotation, scale and articulation and their ability to cope with moderate amounts of within-class deformation. While they attempt to decompose a shape into a set of parts, this decomposition can suffer from ligature-induced instability. In particular, the addition of even a small part can have a dramatic impact on the representation in the vicinity of its attachment. We present an algorithm for identifying and representing the ligature structure, and restoring the non-ligature structures that remain. This leads to a bone graph, a new medial shape abstraction that captures a more intuitive notion of an objectpsilas parts than a skeleton or a shock graph, and offers improved stability and within-class deformation invariance. We demonstrate these advantages by comparing the use of bone graphs to shock graphs in a set of view-based object recognition and pose estimation trials. In this paper we explore the idea of characterizing sentences by the shapes of their structural descriptions only; for example, in the case of context free grammars, by the shapes of the derivation trees only. Such structural descriptions will be called skeletons . A skeleton exhibits all of the grouping structure (phrase structure) of the sentence without naming the syntactic categories used in the description. The inclusion of syntactic categories as variables is primarily a question of economy of description. Every context free grammar is strongly equivalent to a skelet al grammar, in a sense made precise in the paper. Besides clarifying the role of skeletons in mathematical linguistics, we show that skelet al automata provide a characterization of local sets, remedying a “defect” in the usual tree automata theory. We extend the method of skelet al structural descriptions to other forms of tree describing systems. We also suggest a theoretical basis for grammatical inference based on grouping structure only.