Weber Global Statistics Tri- Directional Pattern (WGSTriDP): A Texture Feature Descriptor for Image Retrieval


  • Callins Christiyana Chelladurai Mangayarkarasi College of Engineering
  • Rajamani Vayanaperumal Department of Electronics and Communication Engineering; Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College



Weber Global Statistics Tri-Directional Pattern, Image Retrieval, Texture Feature, Local Patterns, Feature Extraction


The texture is a high-flying feature in an image and has been extracted to represent the image for image retrieval applications. Many texture features are being offered for image retrieval. This paper proposes a local binary pattern-based texture feature called Weber Global Statistics Tri-Directional Pattern (WGSTriDP) to retrieve the images. This pattern combines the advantages of differential excitation components in the Weber Local Binary Pattern (WLBP), sign and magnitude components in the Local Tri-Directional Pattern (LTriDP), and global statistics. Differential Excitation (DE) and Global Statistics TriDirectional Pattern (GSTriDP) are two components of WGSTriDP. The WGSTriDP gains the benefit of discrimination concerning human perception from differential excitation as well as incorporates global statistics into sign and magnitude components in the pattern derived from the local neighborhoods. The effectiveness of the pattern in image retrieval is experimented with in two benchmark databases, such as ORL (face database) and UIUC (texture database). According to the results of the experiments, WGSTriDP outperforms other local patterns in retrieving similar images from the database.