An evolutionary learning based fuzzy theoretic approach for salient object detection

Human attention tends to get focused on the most prominent components of a scene which are in sharp contrast with the background. These are termed as salient regions. The human brain perceives an object to be salient based on various features like the relative intensity, spread of the region, color contrast with the background, size and position within an image. Since these features vary widely, no crisp thresholds can be specified for an automatic salient region detector. In this paper we present a rule based system which uses a set of fuzzy features to mark out the salient region in an image. A genetic algorithm based evolutionary system is used to learn the rules from the training images. Extensive comparisons with the state-of-the-art methods in terms of precision, recall and F-measure are made on two different publicly available datasets to prove the effectiveness of this approach. The application of the proposed salient object detection approach is shown in non-photorealistic rendering, perception based image compression and context aware retargeting applications with promising results.

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