Learn More
We consider image retrieval based on minimum distortion selection of features of color images modelled by Gauss mixtures. The proposed algorithm retrieves the image in a database having minimum distortion when the query image is encoded by a separate Gauss mixture codebook representing each image in the database. We use Gauss mixture vector quantization(More)
We propose a new elastic application model that enables seamless and transparent use of cloud resources to augment the capability of resource-constrained mobile devices. The salient features of this model include the partition of a single application into multiple components called we-blets, and a dynamic adaptation of weblet execution configuration. While(More)
Cloud computing provides elastic computing infrastructure and resources which enable resource-on-demand and pay-as-you-go utility computing models. We believe that new applications can leverage these models to achieve new features that are not available for legacy applications. In our project we aim to build <i>elastic applications</i> which augment(More)
We propose a new elastic application model that enables the seamless and transparent use of cloud resources to augment the capability of resource-constrained mobile devices. The salient features of this model include the partition of a single application into multiple components called weblets, and a dynamic adaptation of weblet execution configuration.(More)
Histogram-based image retrieval requires some form of quan-tization since the raw color images result in large dimen-sionality in the histogram representation. Simple uniform quantization disregards the spatial information among pix-els in making histograms. Since traditional vector quanti-zation (VQ) with squared-error distortion employs only the first(More)
Image retrieval based on color histograms requires quantization of a color space. Uniform scalar quantization of each color channel is a popular method for the reduction of histogram dimensionality. With this method, however, no spatial information among pixels is considered in constructing the histograms. Vector quantization (VQ) provides a simple and(More)
The expectation-maximization (EM) is the dominant algorithm for estimating the parameters of a Gauss mixture (GM). Recently, Gauss mixture vector quantization (GMVQ) based on the Lloyd algorithm has been applied successfully as an alternative for both compression and classification. We investigate the performance of the two algorithms for GM's in image(More)