Corners-based composite descriptor for shapes

In this paper, a composite descriptor for shape retrieval is proposed. The composite descriptor is obtained based upon corner-points and shape region. In an earlier paper, we proposed a composite descriptor based on shape region and shape contour, however, the descriptor was not effective for all perspective and geometric transformations. Hence, we modify the composite descriptor by replacing contour features with corner-points features. The proposed descriptor is obtained from Generic FourierDescriptors (GFD) of the shape region and the GFD ofthe corner-points. We study the performance of the proposed composite descriptor. The proposed method is evaluated using Item S8 within the MPEG-7 Still Images Content Set. Experimental results show that the proposed descriptor is effective.<br /

Big Data of Materials Science - Critical Role of the Descriptor

Statistical learning of materials properties or functions so far starts with a largely silent, non-challenged step: the choice of the set of descriptive parameters (termed descriptor). However, when the scientific connection between the descriptor and the actuating mechanisms is unclear, causality of the learned descriptor-property relation is uncertain. Thus, trustful prediction of new promising materials, identification of anomalies, and scientific advancement are doubtful. We analyse this issue and define requirements for a suited descriptor. For a classical example, the energy difference of zincblende/wurtzite and rocksalt semiconductors, we demonstrate how a meaningful descriptor can be found systematically.Comment: Accepted to Phys. Rev. Let

The EVA spectral descriptor

The EVA descriptor is derived from fundamental IR- and Raman range molecular vibrational frequencies. EVA is sensitive to 3D structure but has an advantage over field-based 3D-QSAR methods inasmuch as it is invariant to both translation and rotation of the structures concerned and thus structural superposition is not required. The latter property and the demonstration of the effectiveness of the descriptor for QSAR means that EVA has been the subject of a great deal of interest from the modelling community. This review describes the derivation of the descriptor, details its main parameters and how to apply them, and provides an overview of the validation that has been done with the descriptor. A recent enhancement to the technique is described which involves the localised adjustment of variance in such a way that enhanced internal and external predictivity may be obtained. Despite the statistical quality of EVA QSAR models the main draw-back to the descriptor at present is the difficulty associated with back-tracking from a PLS model to an EVA pharmacophore. Brief comment is made on the use of the EVA descriptor for diversity studies and the similarity searching of chemical structure databases