New local binary pattern approaches based on color channels in texture classification

Tekin, Ramazan; Ertuğrul, Ömer Faruk; Kaya, Yılmaz

doi:10.1007/s11042-020-09698-5

Cited by 10 publications

(3 citation statements)

References 96 publications

(61 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Liu et al [42] proposed binary rotation invariant and noise tolerant method (BRINT ) method, which take the average value of the pixels in a neighborhood and calculate its binary patterns, making the resulting patterns more resistant to noise. Some variants of the LBP are devised to deal with color images such as color local patterns [43] multichannel decoder-based LBP [43] where a color pixel is treated as a vector having m-components and form a hyperplane and cube diagonal LBP [44], [45]. It is clear that all these methods have their own strengths and weaknesses and their characteristics are complementary to one another.…”

Section: B Lbp Variantsmentioning

confidence: 99%

Gabor Contrast Patterns: A Novel Framework to Extract Features From Texture Images

et al. 2023

View full text Add to dashboard Cite

In this paper, a novel rotation and scale invariant approach for texture classification based on Gabor filters has been proposed. These filters are designed to capture the visual content of the images based on their impulse responses which are sensitive to rotation and scaling in the images. The filter responses are rearranged according to the filter exhibiting the response having largest amplitude, followed by the calculation of patterns after binarizing the responses based on a particular threshold. This threshold is obtained as the average energy of Gabor filter responses at a particular pixel. The binary patterns are converted to decimal numbers, the histograms of which are used as texture features. The proposed features are used to classify the images from two famous texture datasets: Brodatz, CUReT and UMD texture albums. Experiments show that the proposed feature extraction method performs really well when compared with several other state-of-the-art methods considered in this paper and is more robust to noise.

show abstract

Section: B Lbp Variantsmentioning

confidence: 99%

Gabor Contrast Patterns: A Novel Framework to Extract Features From Texture Images

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Other algorithms have been proposed to improve LBPbased results, such as the orthogonal combination of LBP extended to color spaces [55], the local combination adaptive ternary pattern that encodes both color and local information [56], the improved local ternary patterns extended for color properties [57], the color local pattern (CLP) [58], the multichannel adder-based LBP and multichannel decoderbased LBP (mdLBP) [59], the softly quantized color LBP [60], the local binary pattern for color images where a color pixel is treated as a vector having m-components and form a hyperplane [61], and more recently the left to right LBP, the top to down LBP, the curve surface LBP, and the cube diagonal LBP [62], the mean distance LBP combined with color features and co-occurrence matrix [63], the multiple channels LBP that uses both the correlation information among multiple color channels and characteristics in a single color channel [64], and new descriptors called LBPL and LBPL + LBPC that represent color cue as the correlation of pixels after deriving three regression lines in a local window and deriving LBP-like patterns [65].…”

Section: ) Conceptmentioning

confidence: 99%

Color Texture Analysis: A Survey

Humeau-Heurtier

2022

IEEE Access

View full text Add to dashboard Cite

In the field of image processing, texture features and color are fundamental visual cue with complementary roles. They are used in many applications and in a large variety of areas such as quality control, contentbased image retrieval, remote sensing, industrial inspection, surface inspection, object recognition, and medical image analysis. For this purpose, a large number of algorithms have been proposed for texture feature extraction. Some of them are dedicated to gray-scale images while others aim at processing both color and texture. It has been shown that, for many cases, the use of color improves the performance of gray level texture classification. This paper provides a comprehensive survey of the texture feature extraction methods that consider both texture and color information. We propose a categorization of these methods into seven classes, two of them being very recent. For each method, we present the concept, the advantages and drawbacks, and we give examples of application.

show abstract

“…The accuracy of various handcrafted descriptors and deep convolutional neural networks are compared. Various hand-crafted descriptors like Local Binary Pattern (LBP) [10], Histogram of Oriented Gradient (HOG) [11], Locally Encoded Transform Feature Histogram (LETRIST) [12], Gray Level Co-occurrence Matrix (GLCM) [13], Completed Joint scale Local Binary Pattern (CJLBP) [14], Local Tetra Pattern (LTrP) [15], Non-Redundant Local Binary Pattern (NRLBP) [16] are utilized for feature extraction. Deep convolutional neural networks including AlexNet [17], ResNet-50 [18], VGG-19 [19], GoogLeNet [20], Inception v3 [21] CoralNet are being used for the purpose of feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Bag of Features (BoF) Based Deep Learning Framework for Bleached Corals Detection

Jamil

Rahman

Haider

2021

BDCC

View full text Add to dashboard Cite

Coral reefs are the sub-aqueous calcium carbonate structures collected by the invertebrates known as corals. The charm and beauty of coral reefs attract tourists, and they play a vital role in preserving biodiversity, ceasing coastal erosion, and promoting business trade. However, they are declining because of over-exploitation, damaging fishery, marine pollution, and global climate changes. Also, coral reefs help treat human immune-deficiency virus (HIV), heart disease, and coastal erosion. The corals of Australia’s great barrier reef have started bleaching due to the ocean acidification, and global warming, which is an alarming threat to the earth’s ecosystem. Many techniques have been developed to address such issues. However, each method has a limitation due to the low resolution of images, diverse weather conditions, etc. In this paper, we propose a bag of features (BoF) based approach that can detect and localize the bleached corals before the safety measures are applied. The dataset contains images of bleached and unbleached corals, and various kernels are used to support the vector machine so that extracted features can be classified. The accuracy of handcrafted descriptors and deep convolutional neural networks is analyzed and provided in detail with comparison to the current method. Various handcrafted descriptors like local binary pattern, a histogram of an oriented gradient, locally encoded transform feature histogram, gray level co-occurrence matrix, and completed joint scale local binary pattern are used for feature extraction. Specific deep convolutional neural networks such as AlexNet, GoogLeNet, VGG-19, ResNet-50, Inception v3, and CoralNet are being used for feature extraction. From experimental analysis and results, the proposed technique outperforms in comparison to the current state-of-the-art methods. The proposed technique achieves 99.08% accuracy with a classification error of 0.92%. A novel bleached coral positioning algorithm is also proposed to locate bleached corals in the coral reef images.

show abstract

New local binary pattern approaches based on color channels in texture classification

Cited by 10 publications

References 96 publications

Gabor Contrast Patterns: A Novel Framework to Extract Features From Texture Images

Gabor Contrast Patterns: A Novel Framework to Extract Features From Texture Images

Color Texture Analysis: A Survey

Bag of Features (BoF) Based Deep Learning Framework for Bleached Corals Detection

Contact Info

Product

Resources

About