Using mixture modelling to distinguish between low- and high-level factors in natural image viewing

Abstract

The allocation of overt visual attention while viewing photographs of natural scenes is commonly thought to involve both bottom-up feature cues, such as luminance contrast, and top-down factors such as behavioural relevance and scene under- standing. Profiting from the fact that light sources are highly visible but uninformative in visual scenes, we develop a mixture model approach that estimates the relative contribution of various low and high-level factors to patterns of eye movements whilst viewing natural scenes containing light sources. Low-level salience accounts predicted fixations at luminance contrast and at lights, whereas these factors played only a minor role in the observed human fixations. Conversely, human data were mostly explicable in terms of a central bias and a foreground preference. Moreover, observers were more likely to look near lights rather than directly at them, an effect that cannot be explained by low-level stimulus factors such as luminance or contrast. These and other results support the idea that the visual system neglects highly visible cues in favour of less visible object information. Mixture modelling might be a good way forward in understanding visual scene exploration, since it makes it possible to measure the extent that low-level or high- level cues act as drivers of eye movements.

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