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.
@cite_26 proposed a technique called skelet al shape abstraction from examples. A many to many correspondence between the object parts is used to match the object skeletons. This method is invariant to shape articulation and appears to be more suitable for object detection in edge images. However, the tree abstractions are not specific enough to prevent hallucinating of target object in clutter. The dissimilarity value between the object trees is computed by performing many to many matches between the vertices of the trees. Earth Movers Distance (EMD), under L1 norm, is used as a metric for the matching process.
@cite_26
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We describe a novel method for searching and comparing 3D objects. The method encodes the geometric and topological information in the form of a skelet al graph and uses graph matching techniques to match the skeletons and to compare them. The skelet al graphs can be manually annotated to refine or restructure the search. This helps in choosing between a topological similarity and a geometric (shape) similarity. A feature of skelet al matching is the ability to perform part-matching, and its inherent intuitiveness, which helps in defining the search and in visualizing the results. Also, the matching results, which are presented in a per-node basis can be used for driving a number of registration algorithms, most of which require a good initial guess to perform registration. We also describe a visualization tool to aid in the selection and specification of the matched objects. We present a novel hybrid representation for character animation from 4D Performance Capture (4DPC) data which combines skelet al control with surface motion graphs. 4DPC data are temporally aligned 3D mesh sequence reconstructions of the dynamic surface shape and associated appearance from multiple-view video. The hybrid representation supports the production of novel surface sequences which satisfy constraints from user-specified key-frames or a target skelet al motion. Motion graph path optimisation concatenates fragments of 4DPC data to satisfy the constraints while maintaining plausible surface motion at transitions between sequences. Space-time editing of the mesh sequence using a learned part-based Laplacian surface deformation model is performed to match the target skelet al motion and transition between sequences. The approach is quantitatively evaluated for three 4DPC datasets with a variety of clothing styles. Results for key-frame animation demonstrate production of novel sequences that satisfy constraints on timing and position of less than 1p of the sequence duration and path length. Evaluation of motion-capture-driven animation over a corpus of 130 sequences shows that the synthesised motion accurately matches the target skelet al motion. The combination of skelet al control with the surface motion graph extends the range and style of motion which can be produced while maintaining the natural dynamics of shape and appearance from the captured performance. We suggest a novel shape contour descriptor for shape matching and retrieval. The new descriptor is called contour points distribution histogram (CPDH) which is based on the distribution of points on object contour under polar coordinates. CPDH not only conforms to the human visual perception but also the computational complexity of it is low. Invariant to scale and translation are the intrinsic properties of CPDH and the problem of the invariant to rotation can be partially resolved in the matching process. After the CPDHs of images are generated, the similarity value of the images is obtained by EMD (Earth Mover's Distance) metric. In order to make the EMD method used effectively for the matching of CPDHs, we also develop a new approach to the ground distance used in the EMD metric under polar coordinates. Experimental results of image retrieval demonstrate that the novel descriptor has a strong capability in handling a variety of shapes. We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by: (1) solving for correspondences between points on the two shapes; (2) using the correspondences to estimate an aligning transform. In order to solve the correspondence problem, we attach a descriptor, the shape context, to each point. 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, enabling us to solve for correspondences as an optimal assignment problem. Given the point correspondences, we estimate the transformation that best aligns the two shapes; regularized thin-plate splines provide a flexible class of transformation maps for this purpose. The dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points, together with a term measuring the magnitude of the aligning transform. We treat recognition in a nearest-neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image. Results are presented for silhouettes, trademarks, handwritten digits, and the COIL data set.