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Robust rule based local binary pattern method for texture classification and analysis.
| Content Provider | CiteSeerX |
|---|---|
| Author | Devi, O. Rama Prasad, E. V. Reddy, L. S. S. |
| Abstract | Abstract: The primary objective of texture image segmentation is to divide the image into uniform parts. Object extraction; object recognition and object-based compression is typical applications that use texture image segmentation as a low-level image processing. Texture Image segmentation is an important processing step in various image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for texture image segmentation, but it is still complex to assess whether one algorithm produces more accurate segmentation than another for a particular image or set of images, or for a whole category of images. A new texture segmentation approach, including a feature extraction method and the novel segmentation algorithm, is shown in this paper. The proposed feature extraction method called rule based local binary patterns (RLBP), selects the frequently occurred patterns to construct the main pattern set, which avoids the usage of same pattern set for describing different texture structures in traditional local binary patterns. According to the different morphologies and different semantics of texture, the segmentation algorithm is designed for texture segmentation based on RLBP features.As it is simple and efficient so our implementation is suitable for large-scale texture Images. The experiments exhibited the segmentation effect of the proposed method is satisfactory from human visual aspect and segmentation accuracy. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Texture Image Segmentation Robust Rule Local Binary Pattern Method Texture Classification New Texture Segmentation Approach Large-scale Texture Image Traditional Local Binary Pattern Texture Segmentation Low-level Image Processing Different Semantics Whole Category Local Binary Pattern Important Processing Step Typical Application Extensive Research Proposed Feature Extraction Method Accurate Segmentation Segmentation Algorithm Object Extraction Uniform Part Feature Extraction Method Different Texture Structure Main Pattern Segmentation Accuracy Human Visual Aspect Rlbp Feature Computer Vision Application Many Different Approach Object Recognition Different Morphology Object-based Compression Particular Image Various Image Segmentation Effect Primary Objective Novel Segmentation Algorithm |
| Content Type | Text |
| Resource Type | Article |
