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Prior research in scene classification has shown that high-level information can be inferred from low-level image features. Classification rates of roughly 90% have been reported using low-level features to predict indoor scenes vs. outdoor scenes. However, the high classification rates are often achieved by using computationally expensive, high-dimensional(More)
The estimation of the point spread function (PSF) for blur identification, often a necessary first step in the restoration of real images, method is presented. The PSF estimate is chosen from a collection of candidate PSFs, which may be constructed using a parametric model or from experimental measurements. The PSF estimate is selected to provide the best(More)
—In this paper, algorithms for automatic albuming of consumer photographs are described. Specifically, two core algorithms namely event clustering and screening of low-quality images , are introduced and their performance is evaluated. Event clustering and image quality screening have many applications including albuming services, image management and(More)
Memory for visual and verbal material engages widely distributed systems, to a large degree focussed in different hemispheres. It might thus be expected that these disparate neuronal populations should display significantly different characteristics in regard to mnemonic performance. Visual memory, fundamental to all human beings, and whose characteristics(More)
In consumer photography, image appeal may be defined by the interest that a picture generates when viewed by third-party observers. In this paper, the results of a ground truth experiment on human estimation of image appeal are reported, where 11 participants were asked to rank pictures in 30 groups based on their relative appeal within their group and(More)
Manifold learning has been effectively used in computer vision applications for dimensionality reduction that improves classification performance and reduces computational load. Grassmann manifolds are well suited for computer vision problems because they promote smooth surfaces where points are represented as subspaces. In this paper we propose(More)
Scene categorization to indoor vs outdoor may be approached by using low-level features for inferring high-level information about the image. Low-level features such as color and texture have been used extensively in image understanding research, however, they cannot solve the problem completely. In this paper, we propose the use of a Bayesian network for(More)
Current research in content-based semantic image understanding is largely confined to exemplar-based approaches built on low-level feature extraction and classification. The ability to extract both low-level and semantic features and perform knowledge integration of different types of features is expected to raise semantic image understanding to a new(More)