Saliency-guided object proposal for refined salient region detection
Chunlai Wang, Bin Yang · 2016
Automatic detection of visually salient regions across images is useful in many applications. Traditional methods predict saliency values of pixels in a bottom-up fashion and use low-level features. Recent researches demonstrate that high-level information are also important for salient region detection. In this paper, we propose a novel approach of integrating object-level information and bottom-up saliency model. By using saliency-guided object proposals, we implicitly remove the noisy salient regions and produce a refined saliency map. We experimentally show that our approach boosted the performance of bottom-up saliency models and performs favorably against the state-of-the-art methods.