Automatic interesting object extraction from images using complementary saliency maps

Automatic interesting object extraction is widely used in many image applications. Among various extraction approaches, saliency-based ones usually have a better performance since they well accord with human visual perception. However, nearly all existing saliency-based approaches suffer the integrity problem, namely, the extracted result is either a small part of the object (referred to as sketch-like) or a large region that contains some redundant part of the background (referred to as envelope-like). In this paper, we propose a novel object extraction approach by integrating two kinds of "complementary" saliency maps (i.e., sketch-like and envelope-like maps). In our approach, the extraction process is decomposed into two sub-processes, one used to extract a high-precision result based on the sketch-like map, and the other used to extract a high-recall result based on the envelope-like map. Then a classification step is used to extract an exact object based on the two results. By transferring the complex extraction task to an easier classification problem, our approach can effectively break down the integrity problem. Experimental results show that the proposed approach outperforms six state-of-art saliency-based methods remarkably in automatic object extraction, and is even comparable to some interactive approaches.