A Hierarchical Saliency Detection Approach for Bokeh Images

Bokeh is a popular photograph technique that aesthetically highlights the image subject by properly blurring the background contents. While a large number of bokeh images can be found in our albums, the impacts of bokeh on visual saliency has not been studied yet. Our study shows that traditional saliency models do not perform well on bokeh images in that the foreground/background cannot be efficiently differentiated. Therefore, in this paper we propose a hierarchical saliency model for bokeh images through combining local sharpness measure and foreground saliency detection. More specifically, first we use local sharpness feature as a clue to locate the foreground objects in bokeh image so as to get the first level saliency map. Then we compute the second level saliency map from the blurry background using a robust saliency model. In the third step, we can generate the final saliency map by unequally weighted pooling. In order to evaluate the models' performance quantitatively, we build up a bokeh image saliency database. We test the proposed model against traditional models on the bokeh image database. The results indicate that the proposed model systematically outperforms all traditional saliency models.