| 英文标题 | CNN and semantic correlation based co-saliency detection |
| 作者英文名 | Zhang Huadi, Fan Wei, Huang Rui |
| 机构英文名 | College of Computer Science & Technology,Civil Aviation University of China,Tianjin 300300,China |
| 英文摘要 | To solve the problems that the objects with different semantic classes are identified as co-salient objects in current co-saliency detection methods, this paper proposed a CNN and semantic correction-based co-saliency detection method(CSCCD). The proposed method first adopted the guided super pixel filter to process the super pixels obtained by SLIC and the saliency results generated by DSS, which showed clear object boundaries. Then it utilized Mask R-CNN to extract semantic features. It proposed the definitions of image semantic feature and semantic consistency. It also defined the image group semantic correction to solve the problem that detected the objects with different pose belonging to a semantic class as different semantic classes. With the concept, this paper defined image group semantic correlation class, solving semantic correlation problem of multiple images. It generated the final co-saliency detection results by fusing the saliency detection regions with the image group semantic consistent regions. The experimental results on public benchmark datasets show that this algorithm can effectively highlight the whole and outline of the object, and its comprehensive performance in objective quantification is obviously improved. |
| 英文关键词 | co-saliency detection; deep learning; convolutional neural network; image group semantic correlation class |