Sample specific late fusion for saliency detection

Typically, the saliency map of an image is usually inferred by only using the information within this image. While efficient, such single-image-based methods may fail to obtain reliable results, because the information within a single image may be insufficient for defining saliency. In this paper, we propose a novel idea of learning with labeled images and adopt a new paradigm called sample specific late fusion (SSLF). To effectively explore the visual neighborhood information, we propose a semi-supervised learning technique for learning robust sample specific fusion parameters for multiply response maps of generic bottom-up saliency detectors. Different from previous methods, the proposed SSLF method integrates both middle-level image representation and unlabeled data information through an effective graph regularization framework. Extensive experiments have clearly validated its superiority over other state-of-the-art methods.