Exemplar-based methods have proven their efficiency for the reconstruction of missing parts in a digital image. Texture as well as local geometry are often very well restored. Some applications, however, require the ability to reconstruct non local geometric features, e.g. long edges. We propose in this paper to endow a particular instance of exemplar-based method with a geometric guide. The gu...

A novel exemplar-based image inpainting algorithm using the Non-Local Means (NLM) algorithm and Criminisi algorithm is proposed. In order to decrease the error in match rate, the new algorithm adopts the sum of squared differences (SSD) search method for several exemplars to generate a candidate exemplar set. Then it determines the Hausdorff distance as measurement criterion to find a match blo...

Exemplar-based algorithms are a popular technique for image inpainting. They mainly have two important phases: deciding the filling-in order and selecting good exemplars. Traditional exemplar-based algorithms are to search suitable patches from source regions to fill in the missing parts, but they have to face a problem: improper selection of exemplars. To improve the problem we introduction mo...

The early phases of syntactic acquisition are characterized by many input frequency and item effects, which argue against theories assuming innate access to classical syntactic categories. In formulating an alternative view, we consider both prototype and exemplar-learning models of categorization. We argue for a ‘hybrid’ usage-based view in which acquisition depends on exemplar learning and re...

In this paper, we propose a new approach for domain generalization by exploiting the low-rank structure from multiple latent source domains. Motivated by the recent work on exemplar-SVMs, we aim to train a set of exemplar classifiers with each classifier learnt by using only one positive training sample and all negative training samples. While positive samples may come from multiple latent doma...

In a recent article. J. P. Minda and J. D. Smith (2002; see record 2002-00620-002) argued that an exemplar model provided worse quantitative fits than an alternative prototype model to individual subject data from the classic D. L. Medin and M. M. Schaffer (1978) 5/4 categorization paradigm. In addition, they argued that the exemplar model achieved its fits by making untenable assumptions regar...

Exemplar based techniques such as affinity propagation [1] represent data in terms of typical exemplars. This has two benefits: (i) the resulting models are directly interpretable by humans since representative exemplars can be inspected in the same way as data points, (ii) the model can be applied to any dissimilarity measure including non-Euclidean or non-metric settings. Most exemplar based ...

Four experiments examined a distinction between kinds of repetition priming which involve either the identification of the form or meaning of a stimulus or the production of a response on the basis of a cue. Patients with Alzheimer's disease had intact priming on picture-naming and category-exemplar identification tasks and impaired priming on word-stem completion and category-exemplar producti...

Probabilistic models have recently received much attention as accounts of human cognition. However, previous work has focused on formulating the abstract problems behind cognitive tasks and their probabilistic solutions, rather than considering mechanisms that could implement these solutions. Exemplar models are a successful class of psychological process models that use an inventory of stored ...