标题：A saliency detection model combined local and global features
作者：Wang, Pin ;Tian, Guohui ;Chen, Huanzhao
作者机构：[Wang, Pin ;Tian, Guohui ;Chen, Huanzhao ] School of Control Science and Engineering, Shandong University, Jinan; 250061, China
会议名称：2017 Chinese Automation Congress, CAC 2017
会议日期：20 October 2017 through 22 October 2017
来源：Proceedings - 2017 Chinese Automation Congress, CAC 2017
关键词：Convolutional Neural Network; Global Feature; Local Contrast; Saliency Detection
摘要：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. © 2017 IEEE.