Random forest with data ensemble for saliency detection

Seungjun Nah, Kyoung Mu Lee

Abstract: Saliency detection is one of the most active research area in computer vision. Since L. Itti et al. [1] suggested computational model of visual attention, numerous detection algorithms have been proposed. However, most of modern saliency detection methods are based on superpixels which make detection results have abrupt edges inside the salient part. In this paper, we propose pixel-wise detection algorithm that makes more natural detection result. It makes our algorithm excel in describing detailed part of salient objects. Furthermore, we utilize the ensemble of not only random forest but also the data itself. Our algorithm achieves comparable performance with state of the art detection results.