The inherently multidimensional problem of evaluating the complexity of an image is of a certain relevance in both computer science and cognitive psychology. Computer scientists usually analyze spatial dimensions in order to deal with automatic vision problems, such as feature extraction. Psychologists seem more interested in the temporal dimension of complexity, as a means to explore attentional models. Is it possible to define, by merging both approaches, a more general index of visual complexity? The aim of this paper is the definition of objective measures of image complexity that fits with the so named perceived time. Towards the end we have defined a fuzzy mathematical model of visual complexity, based on fuzzy measures of entropy; the results obtained by applying this model to a set of pictorial images present a strong correlation with the outcomes of an experiment with human subjects, based on variation of subjective temporal estimations associated with changes in visual attentional load, which is also described herein.
Cardaci, M., Di Gesu, V., Petrou, M., Tabacchi, M. (2009). A fuzzy approach to the evaluation of image complexity. FUZZY SETS AND SYSTEMS, 160(10), 1474-1484 [10.1016/j.fss.2008.11.017].
A fuzzy approach to the evaluation of image complexity
CARDACI, Maurizio;DI GESU', Vito;TABACCHI, Marco
2009-01-01
Abstract
The inherently multidimensional problem of evaluating the complexity of an image is of a certain relevance in both computer science and cognitive psychology. Computer scientists usually analyze spatial dimensions in order to deal with automatic vision problems, such as feature extraction. Psychologists seem more interested in the temporal dimension of complexity, as a means to explore attentional models. Is it possible to define, by merging both approaches, a more general index of visual complexity? The aim of this paper is the definition of objective measures of image complexity that fits with the so named perceived time. Towards the end we have defined a fuzzy mathematical model of visual complexity, based on fuzzy measures of entropy; the results obtained by applying this model to a set of pictorial images present a strong correlation with the outcomes of an experiment with human subjects, based on variation of subjective temporal estimations associated with changes in visual attentional load, which is also described herein.
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