The current deep learning-based salient object detection algorithms fail to produce accurate object boundaries, which makes the regions along object contours blurred and inaccurate. To solve the problem, a salient object detection algorithm based on multi-task deep learning model was proposed. Firstly, based on deep Convolutional Neural Network (CNN), a multi-task model was used to separately learn region and boundary features of a salient object. Secondly, the detected object boundaries were utilized to produce a number of region candidates. After that the region candidates were re-ranked and their weights were computed by combining the results of salient region detection. Finally, the entire saliency map was extracted. The experimental results on three widely-used benchmarks show that the proposed method achieves better accuracy. According to F-measure, the proposed method averagely outperforms the deep learning-based algorithm by 1.9%, while lowers the Mean Absolutely Error (MAE) by 12.6%.

Concerning the problem that finding community structure in complex network is very complex, a community discovery algorithm based on node similarity was proposed. The basic idea of this algorithm was that node pairs with higher similarity had more posibility to be grouped into the same community. Integrating local and global similarity, it constructed a similarity matrix which each element represents the similarity of a pair of nodes, then merged nodes which have the most similarity to the same community. The experimental results show that the proposed algorithm can get the correct community structure of networks, and achieve better performance than Label Propagation Algorithm (LPA), GN (Girvan-Newman) and CNM (Clauset-Newman-Moore) algorithms in community detection.