Learn More
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)
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)
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)
Region-based image segmentation is one popular approach to image segmentation of generic images. In these methods, an image is partitioned into connected regions by grouping neighboring pixels of similar features, and adjacent regions are then merged with respect to the similarities between the features in these regions. To achieve fine-grain segmentation(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)
Object segmentation is an important preprocessing step for many target recognition applications. Many seg-mentation methods have been studied, but there is still no satisfactory effectiveness measure which makes it hard to compare different segmentation methods, or even different parameterizations of a single method. A good segmentation evaluation method(More)
Image segmentation is a fundamental step in many computer vision applications. Generally, the choice of a segmentation 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)
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)