Supervised orthogonal discriminant projection based on double adjacency graphs for image classification. Issue 11 (4th October 2017)

• Record Type: Journal Article
• Authors: Wang, Bangjun; Zhang, Li; Li, Fanzhang
• Is Part Of: IET image processing. Volume 11:Issue 11(2017)
• Page Start: 1050
• Page End: 1058
• Publication Date: 2017-10-04
• Subjects: image classification -- graph theory -- matrix algebra -- minimisation; weight matrices -- optimal discriminant directions -- local within‐class structure -- nonlocal scatter -- local between‐class scatter -- local within‐class scatter minimisation -- SODP‐DAG -- image classification -- double adjacency graphs -- supervised orthogonal discriminant projection; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0160
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : This study proposes a supervised orthogonal discriminant projection (SODP) based on double adjacency graphs (DAGs). SODP based on DAG (SODP‐DAG) aims to minimise the local within‐class scatter and simultaneously maximise both the local between‐class scatter and the non‐local scatter, where the local between‐class scatter and the local within‐class scatter are constructed by applying the DAG structure. By doing so, SODP‐DAG can keep the local within‐class structure for original data and find the optimal discriminant directions effectively. Moreover, four schemes are designed for constructing weight matrices in SODP‐DAG. To validate the performance of SODP‐DAG, the authors compared it with orthogonal discriminant projection, SODP and others on several publicly available datasets. Experimental results show the feasibility and effectiveness of SODP‐DAG.