Unsupervised Multi-Subclass Saliency Classification for Salient Object Detection

Numerous bottom-up salient object detection algorithms formulate the problem as a classification task. For an input image, these methods usually utilize prior cues to select some regions as training set, and learn a classifier to classify all regions into foreground/background. However, such binary classification based approaches suffer from accuracy problems in some complex scenes. To this end, we propose a novel framework, namely Multi-Subclass Classification with Label Distribution Learning (MSCLDL). Specifically, prior knowledge is firstly employed to build a training set from input image, in which each sample is associated with one of two class labels. Previous works usually learn directly a binary classification model from training set. Different with them, we further decompose two classes into a certain number of subclasses, each sample is thus described by one of multiple subclass labels. Based on the multi-subclass training set, we learn a label distribution model to predict the subclass label of each image region. Furthermore, the saliency value of each image region could be computed via exploring the relationship class and subclass labels. The MSCLDL could overcome the limitation of existing classification-based algorithms in some challenging scenes. Finally, a novel refinement technology is presented to further refine the saliency map obtained by MSCLDL. We compare the proposed method and other state-of-the-art methods on four benchmark datasets, the superiority of our model is adequately demonstrated via the experimental results analysis.