A deep multimodal feature learning network for RGB-D salient object detection. (June 2021)

• Record Type: Journal Article
• Authors: Liang, Fangfang; Duan, Lijuan; Ma, Wei; Qiao, Yuanhua; Miao, Jun
• Is Part Of: Computers & electrical engineering. Volume 92(2021)
• Publication Date: 2021-06
• Subjects: Multimodal feature learning -- Salient object detection -- RGB-D images -- Metric space; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107006
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: In this paper, we propose a deep multimodal feature learning (DMFL) network for RGB-D salient object detection. The color and depth features are firstly extracted from low level to high level feature using CNN. Then the features at the high layer are shared and concatenated to construct joint feature representation of multi-modalities. The fused features are embedded to a high dimension metric space to express the salient and non-salient parts. And also a new objective function, consisting of cross-entropy and metric loss, is proposed to optimize the model. Both pixel and attribute level discriminative features are learned for semantical grouping to detect the salient objects. Experimental results show that the proposed model achieves promising performance and has about 1% to 2% improvement to conventional methods.