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Classic <i>Content-Based Image Retrieval</i> (CBIR) takes a single non-annotated query image, and retrieves similar images from an image repository. Such a search must rely upon a holistic (or global) view of the image. Yet often the desired content of an image is not holistic, but is localized. Specifically, we define <i>Localized Content-Based Image(More)
Image segmentation is an important processing step in many image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation, but it is still difficult to assess whether one algorithm produces more accurate segmentations than another, whether it be for a particular(More)
We explore the application of machine learning techniques to the problem of content-based image retrieval (CBIR). Unlike most existing CBIR systems in which only global information is used or in which a user must explicitly indicate what part of the image is of interest, we apply the multiple-instance (MI) learning model to use a small number of training(More)
Accurate image segmentation is important for many image, video and computer vision applications. Over the last few decades, many image segmentation methods have been proposed. However, the results of these segmentation methods are usually evaluated only visually, qualitatively, or indirectly by the effectiveness of the segmentation on the subsequent(More)
The first step towards the design of video processors and video systems is to achieve an accurate understanding of the major video applications, including not only the fundamentals of the many video compression standards, but also the workload characteristics of those applications. Introduced in 1997, the MediaBench benchmark suite provided the first set of(More)
We describe our experience using reconfigurable ar-chitectures to develop an understanding of an ap-plication's performance and to enhance its performance with respect to customized, constrained logic. We begin with a standard ISA currently in use for embedded systems. We modify its core to measure performance characteristics, obtaining a system that(More)
Image segmentation is a fundamental step in many computer vision applications. Generally, the choice of a seg-mentation algorithm, or parameterization of a given algorithm , is selected at the application level and fixed for all images within that application. Our goal is to create a stand-alone method to evaluate segmentation quality. Stand-alone methods(More)
—In this paper, we present two new methods for efficient rate control and entropy coding in lossy image compression using JPEG-2000. These two methods enable significant improvements in computation complexity and power consumption over the traditional JPEG-2000 algorithms. First, we propose a greedy heap-based rate-control algorithm (GHRaC), which achieves(More)