Transfer learning allows leveraging the knowledge of source domains, available a priori, to help training a classifier for a target domain, where the available data is scarce. The effectiveness of the transfer is affected by the relationship between source and target. Rather than improving the learning, brute force leveraging of a source poorly related to the target may decrease the classifier performance. One strategy to reduce this negative transfer is to import knowledge from multiple sources to increase the chance of finding one source closely related to the target. This work extends the boosting framework for transferring knowledge from multiple sources. Two new algorithms, MultiSourceTrAdaBoost, and TaskTrAdaBoost, are introduced, analyzed, and applied for object category recognition and specific object detection. The experiments demonstrate their improved performance by greatly reducing the negative transfer as the number of sources increases. TaskTrAdaBoost is a fast algorithm enabling rapid retraining over new targets.
Digital imaging systems with extreme zoom capabilities are traditionally found in astronomy and wild life monitoring. More recently, the need for such capabilities has extended to long range surveillance and wide area monitoring such as forest fires, airport perimeters, harbors, and waterways. Auto-focusing is an indispensable function for imaging systems designed for such applications. This paper studies the feasibility of an image based passive auto-focusing control for high magnification systems based on off-the-shelf telescopes and digital cameras/camcorders, with concentration on two associated elements: the cost function (usually the image sharpness measure) and the search strategy. An extensive review of existing sharpness measures and search algorithms is conducted and their performances compared. In addition, their applicability and adaptability to a wide range of high magnifications (50×~1500×) are addressed. This study builds up the foundation for the development of auto-focusing schemes with particular applications to high magnification systems.
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