Hybrid Saliency Detection for Images
Saliency information interpreted from the visual stimuli can predict the attentional behaviour of human perception, thus playing a key role in visual signal processing. In this letter, we present a hybrid saliency detection method for images by which we automatically predict the saliency regions based on low level and high level cues. Unlike existing bottom up and top down attentional methods, we consider the high level cue imposed by the photographer. Based on this assumption, we estimate the defocus map of the image and integrate it with other low level features based on the Bayesian framework. We compare our algorithm to several state of the art saliency detection methods based on the well known 1000 image EPFL database, and demonstrate the superior performance of our proposed algorithm.
IEEE Signal Processing Letters