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Novel Color LBP Descriptors for Scene and Image Texture Classification
| Content Provider | CiteSeerX |
|---|---|
| Author | Liu, Chengjun Verma, Abhishek Banerji, Sugata |
| Abstract | descriptors are presented in this paper for scene image and image texture classification with applications to image search and retrieval. The oRGB-LBP descriptor is derived by concatenating the LBP features of the component images in the oRGB color space. The Color LBP Fusion (CLF) descriptor is constructed by integrating the LBP descriptors from different color spaces; the Color Grayscale LBP Fusion (CGLF) descriptor is derived by integrating the grayscale-LBP descriptor and the CLF descriptor; and the CGLF+PHOG descriptor is obtained by integrating the Pyramid of Histogram of Orientation Gradients (PHOG) and the CGLF descriptor. Feature extraction applies the Enhanced Fisher Model (EFM) and image classification is based on the nearest neighbor classification rule (EFM-NN). The proposed image descriptors and the feature extraction and classification methods are evaluated using three grand challenge databases and are shown to improve upon the classification performance of existing methods. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Component Image Cglf Phog Descriptor Orientation Gradient Novel Color Lbp Descriptor Feature Extraction Different Color Space Enhanced Fisher Model Image Texture Classification Neighbor Classification Rule Orgb-lbp Descriptor Lbp Descriptor Image Classification Classification Method Grand Challenge Database Color Lbp Fusion Lbp Feature Orgb Color Space Image Search Classification Performance Scene Image Clf Descriptor Cglf Descriptor Grayscale-lbp Descriptor Image Descriptor Color Grayscale Lbp Fusion |
| Content Type | Text |
| Resource Type | Article |
