A saliency detection model combined local and global features

Abstract

Most present methods of saliency detection emphasize too much on the local contrast while ignore the global feature of image. The detailed characteristics of the image can be reflected based on the local comparison of image. However, the overall saliency of the image cannot be reflected. In this paper, a saliency detection model combined local and global features was proposed. Firstly, a local feature saliency map was produced by combing background saliency map generated by multi-feature mode and foreground saliency map generated by foreground region contrast method. Then the deep convolution neural network (CNN) was used to train the global image with the center of superpixel block, the label of which is the ground truth of the center superpixel. Thus the global feature saliency map was acquired. The final saliency map was presented by merging local saliency and the global saliency map together. This approach was extensively evaluated on three public datasets including ECSSD, PASCAL-S, MSRA-5000. The higher F-measure (higher is better) as well as lower MAE value (lower is better) than conventional algorithms are obtained in this approach. In addition, the indoor environment images collected in service robot laboratory of Shandong University also obtained a good saliency detection effect through the proposed method.