Some variants of spiral LBP in texture recognition

Kazak, Nihan; Koç, Mehmet

doi:10.1049/iet-ipr.2017.1261

Cited by 10 publications

(3 citation statements)

References 25 publications

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“…The fifth experimental dataset is constructed from UIUC texture database as shown in Figure 10, which involves 25 texture classes and 40 texture images for each class. The resolution of each texture image is 640 × 480 pixels [42]. Similar to the previous database, we reduce the image size to one quarter of the original size.…”

Section: Resultsmentioning

confidence: 99%

Graph wavelet transform for image texture classification

Qiao

Zhao

Song

et al. 2021

IET Image Processing

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Graph is a data structure that can represent complex relationships among data. Graph signal processing, unlike traditional signal processing, explicitly considers the structure and relationship among the signal samples. Graph wavelet transform can provide a multiscale analysis for the graph signal. It is well known that texture is a region property in an image, which is characterized with the intensity and relationship among pixels. In this context of the graph signal processing framework, an image texture can be considered as the signal on the graph. Therefore, a texture classification method based on graph wavelet transform is proposed. Specifically, image textures are decomposed into multiscale components by using two-channel graph wavelet filter banks. Then the local singular value decomposition is applied to each subband. In order to improve the noise-resistant ability, the maximum, mean and median values of the local singular values of graph-wavelet transformation coefficients are extracted. Finally, the Weibull distributions are used to model those extracted values to describe the image textures. The experiments on the benchmark texture datasets are conducted to demonstrate the effectiveness of the proposed method.How to cite this article: Qiao Y-L, Zhao Y, Song C-Y, Zhang K-G, Xiang X-Z. Graph wavelet transform for image texture classification.

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Section: Resultsmentioning

confidence: 99%

Graph wavelet transform for image texture classification

Qiao

Zhao

Song

et al. 2021

IET Image Processing

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“…Machine learning methods have been effectively used with statistical textural measures such as skewness, 42 variance, 43 standard deviation" 44 mean, 45 and gray Level co-occurrence matrix (GLCM). 46 Many statistical descriptors such as local mapped pattern-based descriptor, histogram of oriented gradients (HOG), 46 local energy pattern, 47 local ternary pattern 48 were designed to detect and classify textures at a different scale, illumination, and rotation. The other descriptors for texture analysis include auto correlation, energy, Gabor pattern, and spiking pattern.…”

Section: Texture Analysismentioning

confidence: 99%

Enhanced texture classification through feature compaction using dihybrid bio‐inspired computation techniques

Bagavathi¹,

Saraniya²

2021

Concurrency and Computation

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Synchronous data processing is often preferred considering the volume of data available and the requirement of processed data. Data volume increase from current technical advancements should be balanced and suitably dealt with efficient feature processing techniques. Feature selection can efficiently improve the concurrency in data processing on a completely uncorrelated and separately processable feature set.Texture analysis is a significant application of image processing that analyses specific patterns in an image. As the volume of image data available for producing better classification models progressively moves to be out of the human-manageable range, it is the automation methods that aid the researchers to manage the data. When the number of samples in the data and the dimensionality of the problem is too high, the purpose of data analysis is often compromised. Feature engineering can be utilized to improve the efficiency of the model with a smaller subset of features. This work proposes dibyhrid bio-inspired computation based feature compaction, that is a combination of improvised genetic algorithm and selective tabu search for texture classification problem.Machine learning techniques are employed to evaluate and minimize the validation error from the applied computational methods.

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“…Since a circular neighborhood with unit radius is used, the intensity values of the equally spaced sampling points are calculated using bilinear interpolation when they are not in the center of a pixel. LBP and its variants are used several machine vision problems such as, face recognition [6,15], texture recognition [16][17][18][19][20], object recognition [21], etc. Shape is one of the most basic geometric features that is used to describe the content of the image.…”

Section: Herementioning

confidence: 99%

Histogram of Edge Segment Curvatures for Texture Recognition

Koç¹,

Topal²

2018

Anadolu University Journal of Science and Technology-a Applied Sciences and Engineering

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Texture recognition is one of the active fields in pattern recognition. Researchers have been searching for the best representation of a texture image for decades. The majority of methods use appearance-based properties of texture images to generate a feature descriptor. In this paper, we propose novel feature descriptor, namely histogram of edge segment curvatures (HESC) which extracts edge segments of an input image and construct a histogram from quantized curvature values of them. Therefore, HESC unveils geometric information of texture images by utilizing curve strengths for each pixel along the edge segments. We show that the proposed feature descriptor is robust against rotation and translation. We also extend HESC descriptor to emphasis the contribution of small curvature values. We carry out several experiments in UIUC texture dataset and compare the performance of the proposed HESC descriptor to well-known Local Binary Pattern (LBP). The proposed texture descriptor outperforms LBP in terms of recognition accuracy.

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Some variants of spiral LBP in texture recognition

Cited by 10 publications

References 25 publications

Graph wavelet transform for image texture classification

Graph wavelet transform for image texture classification

Enhanced texture classification through feature compaction using dihybrid bio‐inspired computation techniques

Histogram of Edge Segment Curvatures for Texture Recognition

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