Scale Selective Extended Local Binary Pattern for Texture Classification
In this paper, we propose a new texture descriptor, scale selective extended local binary pattern (SSELBP), to characterize texture images with scale variations. We first utilize multi-scale extended local binary patterns (ELBP) with rotation-invariant and uniform mappings to capture robust local micro- and macro-features. Then, we build a scale space using Gaussian filters and calculate the histogram of multi-scale ELBPs for the image at each scale. Finally, we select the maximum values from the corresponding bins of multi-scale ELBP histograms at different scales as scale-invariant features. A comprehensive evaluation on public texture databases (KTH-TIPS and UMD) shows that the proposed SSELBP has high accuracy comparable to state-of-the-art texture descriptors on gray-scale-, rotation-, and scale-invariant texture classification but uses only one-third of the feature dimension.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationTexture ClassificationSimilar Papers 제목 키워드 기반
Video-Based Facial Expression Recognition Using Local Directional Binary Pattern
Automatic facial expression analysis is a challenging issue and influenced so many areas such as human computer interaction. Due to the uncertainties of the light intensity and light direction, the face gray shades are u…
Facial Expression RecognitionFacial Expression Recognition (FER)Multiscale Fields of Patterns
We describe a framework for defining high-order image models that can be used in a variety of applications. The approach involves modeling local patterns in a multiscale representation of an image. Local properties of a …
Contour DetectionExtended Local Binary Patterns for Efficient and Robust Spontaneous Facial Micro-Expression Recognition
Facial Micro-Expressions (MEs) are spontaneous, involuntary facial movements when a person experiences an emotion but deliberately or unconsciously attempts to conceal his or her genuine emotions. Recently, ME recognitio…
Micro Expression RecognitionMicro-Expression RecognitionNoise Sensitivity of Local Descriptors vs ConvNets: An application to Facial Recognition
The Local Binary Patterns (LBP) is a local descriptor proposed by Ojala et al to discriminate texture due to its discriminative power. However, the LBP is sensitive to noise and illumination changes. Consequently, severa…
SensitivityCompleted Local Derivative Pattern for Rotation Invariant Texture Classification
In this paper, we propose a new texture descriptor, completed local derivative pattern (CLDP). In contrast to completed local binary pattern (CLBP), which involves only local differences at each scale, CLDP encodes the d…
ClassificationTexture Classification