Abstract: A method, system, and computer-readable storage medium for determining an estimate of sensor sensitivity associated with an image. A noise level of an image is determined, then an estimate of sensor sensitivity associated with the image is automatically determined, e.g., by a trained classifier based on the determined noise level. Additionally, the sensor sensitivity estimate can be used to determine scene brightness.

Abstract: A method, system, and computer-readable storage medium for determining an estimate of sensor sensitivity associated with an image. A noise level of an image is determined, then an estimate of sensor sensitivity associated with the image is automatically determined, e.g., by a trained classifier based on the determined noise level. Additionally, the sensor sensitivity estimate can be used to determine scene brightness.

Abstract: A method, system, and computer-readable storage medium for categorizing digital images. A plurality of semantic category scores for a digital image are determined via application of a corresponding plurality of classifiers. A semantic category profile for the image is automatically determined based on the plurality of semantic category scores, where the semantic category profile characterizes semantic content of the image, and is useable to perform semantic based operations with respect to the image.

Abstract: Provided are a method, system, and computer readable medium for training and using classification components on multiple processing units. A plurality of processing units each has a memory including one of a plurality of subsets of a set of data points. At least two of the processing units have different subsets of data points. A plurality of classification components are executed by the processing units. Classification components executing at the processing units are trained, wherein each classification component is trained with the subset of data points in the memory of the processing unit that is executing the classification component. One of the classification components is transferred to an additional processing unit of the processing units to train the transferred classification component using the subset of data points in the memory at the additional processing unit in response to training the classification component with the subset of data points.

Abstract: Provided are a method, system, and computer readable medium for training and using classification components on multiple processing units. A plurality of processing units each has a memory including one of a plurality of subsets of a set of data points. At least two of the processing units have different subsets of data points. A plurality of classification components are executed by the processing units. Classification components executing at the processing units are trained, wherein each classification component is trained with the subset of data points in the memory of the processing unit that is executing the classification component. One of the classification components is transferred to an additional processing unit of the processing units to train the transferred classification component using the subset of data points in the memory at the additional processing unit in response to training the classification component with the subset of data points.