Salient object detection by multi-level features learning determined sparse reconstruction

We propose a salient object detection algorithm via multilevel features learning determined sparse reconstruction. There are three stages in our method. First, the test image are successively processed by a segmentation and semantic information generation procedures. Second, three kinds of features are extracted from semantic, global, and local levels for each superpixel to train a random forest regressor, the learned regression model is then used to generate an initial saliency map. Third, the ultimate detection result is produced using sparse reconstruction determined by the initial saliency map. Compared with most approaches, the proposed method has two obvious advantages. First, the heterogeneous regions inside salient object are often allocated similar saliency values in saliency map. Second, there are much fewer false positives in our detection results. The superior performance of our method were evaluated on four datasets with 12 state-of-the-art approaches.