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2 February 2006 Machine learning of human responses to images
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The human user is an often ignored component of the imaging chain. In medical diagnostic tasks, the human observer plays the role of the decision-maker, forming opinions based on visual assessment of images. In content-based image retrieval, the human user is the ultimate judge of the relevance of images recalled from a database. We argue that data collected from human observers should be used in conjunction with machine-learning algorithms to model and optimize performance in tasks that involve humans. In essence, we treat the human observer as a nonlinear system to be identified. In this paper, we review our work in two applications of this general idea. In the first, a learning machine is trained to predict the accuracy of human observers in a lesion detection task for purposes of assessing image quality. In the second, a learning machine is trained to predict human users' perception of the similarity of two images for purposes of content-based image retrieval from a database. In both examples, it is shown that a nonlinear learning machine can accurately identify the nonlinear human system that maps images into numerical values, such as detection performance or image similarity.
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Miles N. Wernick, Yongyi Yang, Jovan G. Brankov, Liyang Wei, Nikolas P. Galatsanos, and Issam El-Naqa "Machine learning of human responses to images", Proc. SPIE 6065, Computational Imaging IV, 60650S (2 February 2006); doi: 10.1117/12.658072;

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