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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)
—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)
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)
Prior research in scene classi cation has focused on mapping a set of classic low-level vision features to semantically meaningful categories using a classi er engine. In this paper, we propose improving the established paradigm by using a simpli ed low-level feature set to predict multiple semantic scene attributes that are integrated probabilistically to(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)
This paper exploits the discriminative power of manifold learning in conjunction with the parsimonious power of sparse signal representation to perform robust facial expression recognition. By utilizing an &#x2113;<sup>1</sup> reconstruction error and a statistical mixture model, both accuracy and tolerance to occlusion improve without the need to perform(More)
Embedded vision systems, such as smart cameras, provide a new framework for computer vision algorithms in resource constrained environments. In this paper, we present a new object tracking methodology based on random projections, which offers the benefits of fast, low-complexity transformation of the input data into accurate and computationally attractive(More)