Weber local descriptor for image analysis and recognition: a survey
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SURVEY
Weber local descriptor for image analysis and recognition: a survey Arnab Banerjee1 · Nibaran Das1 · K. C. Santosh2 Accepted: 4 November 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Weber local descriptor (WLD) is applied for addressing the challenges in image/pattern problems, especially in computer vision and pattern recognition domains. In this paper, we review literature on theories and applications of WLD. Using WLD, we address the different challenges of image analysis and recognition features with respect to illumination changes, contrast differences, and geometrical transformations like rotation, scaling, translation, and mirroring. Further, the role of the classifiers and experimental protocols used in the different applications are discussed. Applications include texture classification, medical imaging, agricultural safety, fingerprint analysis, forgery analysis, and face recognition. Keywords Weber local descriptor · Image analysis and recognition · Computer vision and pattern recognition
1 Introduction Extensive researches were done on image recognition, where texture analysis-based local and global texture descriptors were employed. Texture provides knowledge about spatial distribution of color and intensities either from the whole image or from a region-of-interest that can be based on the point-based descriptors. Texture analysis also helps in segmenting images into meaningful regions and in classifying them accordingly. Structural approaches reveal that texture is a collection of primitive texels with patterns whereas statistical approaches extract the computable measure of the intensity values in the image. Several texture descriptors were proposed and applied on several different applications viz. computer vision, pattern recognition, material classification, face recognition, emotion analysis, and medical image analysis. Multiple texture models viz. statistical model (co-occurrence matrix, auto-correlation features etc.), geo-
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K. C. Santosh [email protected] Arnab Banerjee [email protected] Nibaran Das [email protected]
1
Jadavpur University, Kolkata, West Bengal 700032, India
2
University of South Dakota, Vermillion, SD 57069, USA
metric models (structural model, Voronoi tessellation etc.), model-based random fields (fractal approach, random field model etc.), and signal processing-based models (Gabor and wavelet method, spatial domain filters, etc.) exist in the literature. Due to robustness and improved execution time, local descriptors are widely used in texture recognition. Further, sparse and dense descriptors are commonly used to classify texture. Primarily, their difference lies in the way they describe the image. Sparse descriptors detect key points to sample a local region and dense descriptors extract local features from all pixels.The feature transformation technique with scale invariance (SIFT) [49] and rotational invariance [44], Histogram of Oriented Gradients(HOG) are the key sparse descriptors. If we see the dense descriptor, G
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