Local directional mask maximum edge patterns for image retrieval and face recognition. Issue 3 (1st April 2016)

• Record Type: Journal Article
• Authors: Vipparthi, Santosh Kumar; Murala, Subrahmanyam; Gonde, Anil Balaji; Jonathan Wu, Q.M.
• Is Part Of: IET computer vision. Volume 10:Issue 3(2016)
• Page Start: 182
• Page End: 192
• Publication Date: 2016-04-01
• Subjects: feature extraction -- image retrieval -- face recognition -- filtering theory; local directional mask maximum edge patterns -- feature descriptor -- face recognition applications -- local binary pattern -- LBP variants -- noise variations -- magnitude directional edges -- DE -- multiresolution Gaussian filters -- studies-magnetic imaging -- resonance imaging -- Brodatz databases -- MIT databases -- VisTex databases -- Extended Yale B databases -- biomedical image retrieval -- texture retrieval; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2015.0035
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : This study proposes a new feature descriptor, local directional mask maximum edge pattern, for image retrieval and face recognition applications. Local binary pattern (LBP) and LBP variants collect the relationship between the centre pixel and its surrounding neighbours in an image. Thus, LBP based features are very sensitive to the noise variations in an image. Whereas the proposed method collects the maximum edge patterns (MEP) and maximum edge position patterns (MEPP) from the magnitude directional edges of face/image. These directional edges are computed with the aid of directional masks. Once the directional edges (DE) are computed, the MEP and MEPP are coded based on the magnitude of DE and position of maximum DE. Further, the robustness of the proposed method is increased by integrating it with the multiresolution Gaussian filters. The performance of the proposed method is tested by conducting four experiments onopen access series of imaging studies‐magnetic resonance imaging, Brodatz, MIT VisTex and Extended Yale B databases for biomedical image retrieval, texture retrieval and face recognition applications. The results after being investigated the proposed method shows a significant improvement as compared with LBP and LBP variant features in terms of their evaluation measures on respective databases.