With the rapid increase in the available data, it becomes computationally harder to extract useful information. Thus, several techniques like PCA were proposed to embed high-dimensional data into lowdimensional latent space. However, these techniques don’t take the data relations into account. This motivated the development of other techniques like MDS and LLE which preserve the relations betwe...

In the talk I will presents an approach of visual shape recognition based onexemplars of attributed keypoints. The work may be seen as a refinementof a previous work [1]. The work is aimed at object shape categorizationby image semantic report. Training is performed by storing exemplarsof keypoints detected in labeled training images. Recognition is made bykeypoint matching ...

Clustering is often formulated as the maximum likelihood estimation of a mixture model that explains the data. The EM algorithm widely used to solve the resulting optimization problem is inherently a gradient-descent method and is sensitive to initialization. The resulting solution is a local optimum in the neighborhood of the initial guess. This sensitivity to initialization presents a signifi...

This paper describes a prototype system for the prediction of parameters associated with a geological prospect, required for assessing the likelihood of hydrocarbons at the prospect. A principle characteristic of expert reasoning in this domain involves the recall and use of previous examples (`exemplars') of similar, already-drilled wells in addition to the use of general rules concerning know...

Exemplar-based texture synthesis is the process of generating, from an input sample, new texture images of arbitrary size and which are perceptually equivalent to the sample. The two main approaches are statisticsbased methods and patch re-arrangement methods. In the first class, a texture is characterized by a statistical signature; then, a random sampling conditioned to this signature produce...

Recovering lost part of an image plays a great role in image processing. Inpainting is a technique that helps in recovering lost pixels from an image. From the existing techniques of Inpainting, Exemplar Inpainting is one of the fast and better techniques that help in restoring the lost part of an image. Exemplar based method chooses a patch similar to the lost patch from the known area to fill...

Exemplar-based learning algorithms are often faced with the problem of deciding which instances or other exemplars to store for use during generalization. Storing too many exemplars can result in large memory requirements and slow execution speed, and can cause an oversensitivity to noise. This paper has two main purposes. First, it provides a survey of existing algorithms used to reduce the nu...

Posterior features have been shown to yield very good performance in multiple contexts including speech recognition, spoken term detection, and template matching. These days, posterior features are usually estimated at the output of a neural network. More recently, sparse representation has also been shown to potentially provide additional advantages to improve discrimination and robustness. On...

This study describes integration of an articulatory factor into the exemplar-based Context Sequence Model of speech production, CSM [10], which builds on the concept of a speech perceptionproduction loop. It has been demonstrated that selection of new exemplars for speech production is based on about 0.5 s of preceding acoustic context and following linguistic match of the exemplars. This inves...

Image Inpainting is the process of filling in missing regions in an image. The objective of inpainting is to reconstruct the missing regions in a visually plausible way. Several algorithms are available in the literature for the same. Many researchers have proposed a large variety of exemplar based image inpainting algorithms to restore the structure and texture of damaged images. In this paper...