localbinarypattern is a new texture measure which is theoretically simply but powerful. When used in remote sensing multi-channel image processing, the multivariate version of LBP operator should be considered. In t...
详细信息
ISBN:
(纸本)9780780395091
localbinarypattern is a new texture measure which is theoretically simply but powerful. When used in remote sensing multi-channel image processing, the multivariate version of LBP operator should be considered. In this paper, a multivariate LBP operator was proposed to calculate multivariate texture for multi-spectral remote sensing image. Both single-band texture and multivariate texture were employed in the classification process. Segmented Minimum Noise Fraction transform was conducted before classification to reduce features. Experiment shows that compared to spectral classification, the classification accuracy can be significantly improved when the proposed classification scheme was used.
Texture is the surface property that is used to identify and recognise objects. This property is widely used in many applications including texture-based face recognition systems, surveillance, identity verification a...
详细信息
Texture is the surface property that is used to identify and recognise objects. This property is widely used in many applications including texture-based face recognition systems, surveillance, identity verification and so on. The localbinarypattern (LBP) texture method is most successful for face recognition. Owing to the great success of LBP, recently many models, which are variants of LBP have been proposed for texture analysis. Some of the derivatives of LBPs are multivariate local binary pattern, centre symmetric localbinarypattern, localbinarypattern variance, dominant localbinarypattern, advanced localbinarypattern, local texture pattern (LTP) and local derivative pattern (LDP). In this scenario, it is essential to review, whether LBP or their derivatives perform better for face recognition. The real-time challenges such as illumination changes, rotations, angle variations and facial expression variations are evaluated by different LBP-based models. Experiments were conducted on the Japanese female facial expression, YALE and FRGC version2 databases. The results show that LDP and LTP perform much better than the other LBP-based models.
暂无评论