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Image salient object detection is useful for applications like image retrieval, object recognition etc. Aiming to
obtain the pixel-level saliency map, most traditional saliency models ignore the object-level information and become inappropriate
in complex scenes. A new salient object detection method is proposed in this paper by incorporating the notion
of object and background prior directly into the saliency measurement. Firstly the collection of proto-objects is obtained
by adapting selective search method. Secondly for each proto-object, we measure its saliency score by computing
the distance of histogram feature between the proto-object and the prior background region, then the saliency scores are
ranked, and the top-ranked proto-object is considered as the salient object. The publicly MSRA dataset is adapted for experiment
evaluation, compared with several other state-of-the-art methods, the proposed method produces superior performance.
Experimental results show that the proposed method is effective.