Multiresolution LDBP descriptors for texture classification using anisotropic diffusion with an application to wood texture analysis

Hiremath, P. S.; Bhusnurmath, Rohini A.

doi:10.1016/j.patrec.2017.01.015

Cited by 18 publications

(9 citation statements)

References 18 publications

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“…In our experiments, we used several feature extraction methods to validate the ability of our method to distinguish between smoke and non-smoke images on the three test sets. These compared methods are DRLBP [33], CLBP [16], LDBP [34], PLBP [35], PRICoLBP [36], MDLBP [37] LTrP [38] and DFD [39]. The compared LBP variants are all un-mapped for fair comparisons.…”

Section: Analysis Of Resultsmentioning

confidence: 99%

Dual-Encoded Features from Both Spatial and Curvelet Domains for Image Smoke Recognition

2019

KSII TIIS

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Visual smoke recognition is a challenging task due to large variations in shape, texture and color of smoke. To improve performance, we propose a novel smoke recognition method by combining dual-encoded features that are extracted from both spatial and Curvelet domains. A Curvelet transform is used to filter an image to generate fifty sub-images of Curvelet coefficients. Then we extract Local Binary Pattern (LBP) maps from these coefficient maps and aggregate histograms of these LBP maps to produce a histogram map. Afterwards, we encode the histogram map again to generate Dual-encoded Local Binary Patterns (Dual-LBP). Histograms of Dual-LBPs from Curvelet domain and Completed Local Binary Patterns (CLBP) from spatial domain are concatenated to form the feature for smoke recognition. Finally, we adopt Gaussian Kernel Optimization (GKO) algorithm to search the optimal kernel parameters of Support Vector Machine (SVM) for further improvement of classification accuracy. Experimental results demonstrate that our method can extract effective and reasonable features of smoke images, and achieve good classification accuracy.

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Section: Analysis Of Resultsmentioning

confidence: 99%

Dual-Encoded Features from Both Spatial and Curvelet Domains for Image Smoke Recognition

2019

KSII TIIS

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“…At present, the color classification of wood usually begins from two directions: one is to extract better feature values, and the other is to select a more accurate classifier. Some studies have designed better color characteristics to describe the wood color [ 3 , 4 , 5 , 6 ]. Barmpoutis [ 7 ] proposed a method to enable the representation of wood images as concatenated histograms of higher-order linear dynamical systems produced by vertical and horizontal image patches; Hiremath [ 8 ] proposed an efficient multiresolution method for texture classification based on anisotropic diffusion and local directional binary patterns (LDBP).…”

Section: Introductionmentioning

confidence: 99%

Online Color Classification System of Solid Wood Flooring Based on Characteristic Features

Zhuang

Liu

Ding

et al. 2021

Sensors

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Solid wood flooring has good esthetic properties and is an excellent material for interior decoration. To meet the artistic effects of specific interior decoration requirements, the color of solid wood flooring needs to be coordinated. Thus, the color of the produced solid wood flooring needs to be sorted to meet the individual needs of customers. In this work, machine vision, deep learning methods, and ensemble learning methods are introduced to reduce the cost of manual sorting and improve production efficiency. The color CCD camera was used to collect 108 solid wood floors of three color grades provided by the company and obtained 108 18,000 × 2048 pixel wood images. A total of 432 images were obtained after data expansion. Deep learning methods, such as VGG16, DenseNet121, and XGBoost, were compared. After using XGBoost to filter the features, the accuracy of solid wood flooring color classification was 97.22%, the training model time was 5.27 s, the average test time for each picture was 51 ms, and a good result was achieved.

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“…These models can be divided into single bands, if the data of each channel is considered separately, or a multiband, if two or more channels are considered together. The advantage of the single-band approach is the easy adaptation of classical models based on a grayscale domain, such as Gabor filters [15,[30][31][32], local binary patterns (LBP) or variants [5,7,8,[33][34][35][36][37][38], Galois fields [39], or Haralick statistics [3]. In Reference [2], the main objective was to determine the contribution of color information for the overall performance of classification using Gabor filters and co-occurrence measures, yielding results almost 10% better than those obtained with only grayscale images.…”

Section: Introductionmentioning

confidence: 99%

“…The experimental results were tested on the Outex and KTH-TIPS2b databases reaching 95.8% and 91.3%, respectively. In References [32,33,36,39], the methods were not tested in the complete databases. In References [26,45,46], the methods used a different metric to calculate the classification.…”

Section: Introductionmentioning

confidence: 99%

Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing

Navarro-Clavería¹

2019

Applied Sciences

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Many applications in image analysis require the accurate classification of complex patterns including both color and texture, e.g., in content image retrieval, biometrics, and the inspection of fabrics, wood, steel, ceramics, and fruits, among others. A new method for pattern classification using both color and texture information is proposed in this paper. The proposed method includes the following steps: division of each image into global and local samples, texture and color feature extraction from samples using a Haralick statistics and binary quaternion-moment-preserving method, a classification stage using support vector machine, and a final stage of post-processing employing a bagging ensemble. One of the main contributions of this method is the image partition, allowing image representation into global and local features. This partition captures most of the information present in the image for colored texture classification allowing improved results. The proposed method was tested on four databases extensively used in color–texture classification: the Brodatz, VisTex, Outex, and KTH-TIPS2b databases, yielding correct classification rates of 97.63%, 97.13%, 90.78%, and 92.90%, respectively. The use of the post-processing stage improved those results to 99.88%, 100%, 98.97%, and 95.75%, respectively. We compared our results to the best previously published results on the same databases finding significant improvements in all cases.

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Multiresolution LDBP descriptors for texture classification using anisotropic diffusion with an application to wood texture analysis

Cited by 18 publications

References 18 publications

Dual-Encoded Features from Both Spatial and Curvelet Domains for Image Smoke Recognition

Dual-Encoded Features from Both Spatial and Curvelet Domains for Image Smoke Recognition

Online Color Classification System of Solid Wood Flooring Based on Characteristic Features

Color–Texture Pattern Classification Using Global–Local Feature Extraction, an SVM Classifier, with Bagging Ensemble Post-Processing

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