Xinchao Li

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We measured human observers' detectability of aperiodic signals in noise with two components (white and low-pass Gaussian). The white-noise component ensured that the signal detection task was always noise limited rather than contrast limited (i.e., image noise was always much larger than observer internal noise). The low-pass component can be considered to(More)
Predicting geographic location using exclusively the visual content of images holds the promise of greatly benefiting users' access to media collections. In this paper, we present a visual-content-based approach that predicts where in the world a social image was taken. We employ a ranking method that assigns a query photo the geo-location of its most(More)
Spatial verification is a key step in boosting the performance of object-based image retrieval. It serves to eliminate unreliable correspondences between salient points in a given pair of images, and is typically performed by analyzing the consistency of spatial transformations between the image regions involved in individual correspondences. Spatial(More)
In this paper, we present a visual-content-based approach that predicts where in the world a social image was tak-en. We employ a ranking method that assigns a query photo the geo-location of its most likely geo-visual neighbor in the social image collection. The experiments carried out on the MediaEval Placing Task 2013 data set support the conclusion that(More)
INTRODUCTION On July 1, 2011, the Chinese government launched a national Action Plan for antibiotic stewardship targeting antibiotic misuse in public hospitals. The aim of this study was to evaluate the impacts of the Action Plan in terms of frequency and intensity of antibiotic utilization and patients costs in public general hospitals. METHODS(More)
Estimating the geo-location of an image or video is an interesting and challenging task in information retrieval and computer vision. In this paper, a pure image content based approach for this task is described. We partition the world map into regions based on external data sources (climate and biomes data). We hypothesize that such a partition yields(More)
In this paper, we present a subclass-representation approach that predicts the probability of a social image belonging to one particular class. We explore the co-occurrence of user-contributed tags to find subclasses with a strong connection to the top level class. We then project each image on to the resulting subclass space to generate a subclass(More)