In this paper we are presenting a semi-automated schema matching approach named C3SM (Conceptual and Context based Combination of Schema Matchers). A single schema matching technique is unlikely to be successful to achieve high match accuracy for a large variety of schemas therefore C3SM combines different matchers. C3SM exploits various types of schema information, e.g. element names, data types and structural properties. It also utilizes auxiliary sources, such as taxonomies, dictionaries, thesauri or synonym tables. In order to manage different types of heterogeneities and to increase the accuracy of the similarity assertions generated, matching between elements is performed in the contexts in which they are modeled and similarity is established on the basis of the concepts that the elements model.
Recently, several studies have shown that linear representation based approaches are very effective and efficient for image classification. One of these linear-representation-based approaches is the Collaborative representation (CR) method. The existing algorithms based on CR have two major problems that degrade their classification performance. First problem arises due to the limited number of available training samples. The large variations, caused by illumintion and expression changes, among query and training samples leads to poor classification performance. Second problem occurs when an image is partially noised (contiguous occlusion), as some part of the given image become corrupt the classification performance also degrades. We aim to extend the collaborative representation framework under limited training samples face recognition problem. Our proposed solution will generate virtual samples and intra-class variations from training data to model the variations effectively between query and training samples. For robust classification, the image patches have been utilized to compute representation to address partial occlusion as it leads to more accurate classification results. The proposed method computes representation based on local regions in the images as opposed to CR, which computes representation based on global solution involving entire images. Furthermore, the proposed solution also integrates the locality structure into CR, using Euclidian distance between the query and training samples. Intuitively, if the query sample can be represented by selecting its nearest neighbours, lie on a same linear subspace then the resulting representation will be more discriminate and accurately classify the query sample. Hence our proposed framework model the limited sample face recognition problem into sufficient training samples problem using virtual samples and intra-class variations, generated from training samples that will result in improved classification accuracy as evident from experimental results. Moreover, it compute representation based on local image patches for robust classification and is expected to greatly increase the classification performance for face recognition task.
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