Saliency object detection: integrating reconstruction and prior

Abstract

To remedy some challenging cases in saliency detection such as complex background and multiple objects. A new saliency object detection approach is proposed via integrating reconstruction and prior knowledge. This paper first segments each image into super pixels using over-segmentation algorithm. Then, the reconstruction saliency map and prior saliency map are generated by reconstruction and prior, respectively. The reconstruction involves dense reconstruction and sparse reconstruction. When the saliency object appears on the image boundaries, the detection can be more accurate via dense reconstruction. In addition, if there is complex background in natural scene image, the sparse reconstruction can be more robust and suppress the background effectively. The prior adopts background prior and center prior, which can highlight the saliency object uniformly. The reconstruction saliency map and prior saliency map are nonlinearly integrated to generate the final saliency map. The proposed method is compared with the other five state-of-the-art algorithms based on comprehensive metrics. The experimental results demonstrate that the proposed algorithm has superior saliency detection performance and low average elapsing time.