iccv iccv2013 iccv2013-416 iccv2013-416-reference knowledge-graph by maker-knowledge-mining
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Author: Michael Gygli, Helmut Grabner, Hayko Riemenschneider, Fabian Nater, Luc Van_Gool
Abstract: We investigate human interest in photos. Based on our own and others ’psychological experiments, we identify various cues for “interestingness ”, namely aesthetics, unusualness and general preferences. For the ranking of retrieved images, interestingness is more appropriate than cues proposed earlier. Interestingness is, for example, correlated with what people believe they will remember. This is opposed to actual memorability, which is uncorrelated to both of them. We introduce a set of features computationally capturing the three main aspects of visual interestingness that we propose and build an interestingness predictor from them. Its performance is shown on three datasets with varying context, reflecting diverse levels of prior knowledge of the viewers.
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