Application of kernel-genetic algorithm as nonlinear feature selection in tropical wood species recognition system

Yusof, Rubiyah; Khalid, Marzuki; Khairuddin, Anis Salwa Mohd

doi:10.1016/j.compag.2013.01.007

Cited by 52 publications

(46 citation statements)

References 25 publications

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“…Species/ hybrid the number of features and number of classes, as shown by Yusof et al (2013) and Paula et al (2014).…”

Section: Originmentioning

confidence: 99%

“…Among the first group, the works of Piuri & Scotti (2010), Oliveira et al (2015), and Nisgoski et al (2017a, b) rank high. Among the image-analysis studies, those of Khalid et al (2008), Wang et al (2013), Yusof et al (2013), Paula et al (2014), Martins et al (2015), Zamri et al (2016), and Ibrahim et al (2017) are eminent. Both techniques revealed acceptable results.…”

Section: Introductionmentioning

confidence: 99%

“…Both techniques revealed acceptable results. In general, the analysis of images appears to offer more promise, with satisfactory results when large databases are used, as evident from the research by Yusof et al (2013) who, in their classification of 52 woody species, achieved 98.67% accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Potential of Texture Analysis for Charcoal Classification

et al. 2019

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Charcoal produced from reforested wood can be distinguished from the charcoal derived from the wood of native species. This identification is very important for the trade, control and monitoring of charcoal production in Brazil. This study investigated the potential of texture analysis for classifying the charcoal based on origin (eucalyptus or native) and species. A total of 17 wood species were studied, five of which belonged to genus Eucalyptus and 12 were native to the Zona da Mata Mineira. Texture features based on the gray level co-occurrence matrix were extracted from digital images. The linear discriminant analysis was used to classify the images with these features. Employing 10 features, 96.2% accuracy was achieved for the classification by origin and 90.4% for the categorization by species. Texture analysis was shown to be a favorable and effective method that could facilitate the establishment of semiautomated techniques to classify the charcoal based on origin or species.

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“…Species/ hybrid the number of features and number of classes, as shown by Yusof et al (2013) and Paula et al (2014).…”

Section: Originmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Potential of Texture Analysis for Charcoal Classification

et al. 2019

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“…As a method for the extraction of image information, which is one of the important processes in image recognition, a texture analysis has been utilized in a variety of fields such as remote sensing and medical imaging. Several authors have also reported attempts of the application of these techniques to wood identification [7][8][9][10][11][12][13][14][15][16][17][18][19], which are particularly active in tropical areas. Tropical timber is an important biological and economical resource in the developing world; thus, wood identification is demanded at trading locations to circulate proper wood in the market as well as to keep illegally logged timber under observation.…”

Section: Introductionmentioning

confidence: 99%

“…Several approaches for the extraction of image information were tested, such as the segmentation of specific anatomical features [10,11], the gray-level cooccurrence matrix (GLCM) [7][8][9], Gabor filtering [16], Gabor filtering followed by the GLCM [12], the Coiflet discrete wavelet transform [18], the extended higher local order autocorrelation [15], and local binary patterns [14]. Combinations of multiple extractors were also tested for further improvement in the systems [13,17,19].…”

Section: Introductionmentioning

confidence: 99%

Automated recognition of wood used in traditional Japanese sculptures by texture analysis of their low-resolution computed tomography data

et al. 2015

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The identification of wood species used in the cultural artifacts is important in terms of their preservation and inheritance. However, a nondestructive method is required, and wood samples must be partly cut off in conventional methods such as microscopy. In this study, we constructed a novel system for wood identification using image recognition of X-ray computed tomography images of eight major species used in Japanese wooden sculptures. Texture analyses of the computed tomography images were carried out using the gray-level co-occurrence matrix, from which 15 textural features were calculated. The k-nearest-neighbor algorithm combined with cross validation was applied for classification and evaluation of the system. Input datasets with a variation in image qualities (resolution, gray level, and image size) were investigated using this novel system, and the accuracy was greater than 98 % when the input images had a certain quality level. Although there are still technical problems to be overcome, progress in the development of automated identification is extremely encouraging in that such an approach has the potential to make a valuable contribution in adding scientific species notion to the artifacts; otherwise, only the literal documents are available.

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Intelligent classification and analysis of regional landforms based on automatic feature selection

Xu,

Zhu,

et al. 2023

Earth Surf Processes Landf

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Terrain features are an important basis for realizing high‐precision landform classification, and feature selection is a key step of machine learning and knowledge mining. However, the feature selection process is facing challenges due to the multidimensionality and correlation of multisource terrain feature datasets and factors. Traditional feature selection methods lack enough consideration for the interpretability and transparency of feature factors, but the transparent decision‐making process of feature selection precisely determines the modelling effect and the reliability of model application results. Current research urgently needs to work out the black holes of visual representation during feature selection. In the process of intelligent landform classification, multiple and effective terrain feature is an essential factor in enhancing the performance and generalisation ability of the network. Therefore, we initially selected 40 terrain feature parameters, including basic terrain factors and digital elevation model (DEM) terrain textures, to calculate the feature contribution degree and sort the parameter importance based on the SHapley Additive exPlanations (SHAP) method, then reserved 10%, 20%, 30%, 40% and 50% terrain features in turn for constructing the landform classification dataset. Because the traditional UNet network cannot completely capture abrupt landform features, the convolutional block attention module (CBAM) was integrated into the UNet, and a deep learning model was established for the fine‐grained classification of regional landforms. Considering the calculation rate, even though there are large regional spatial differences and genetic mechanisms, it is appropriate to retain 20% of terrain features for intelligent landform classification. The classification accuracy of typical regions, namely, the Hanzhong Basin, North China Plain, Yunnan–Guizhou Plateau and Tibetan Plateau, reached 98.76%, 97.36%, 96.3% and 92.78%, respectively, and what's more, some accuracies went up to a higher level under other feature combinations. Meanwhile, given the different feature combinations corresponding to regional landform types, the combinative stability and spatial orderliness characteristics were explored to explain the accuracy variation trend.

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Application of kernel-genetic algorithm as nonlinear feature selection in tropical wood species recognition system

Cited by 52 publications

References 25 publications

Potential of Texture Analysis for Charcoal Classification

Potential of Texture Analysis for Charcoal Classification

Automated recognition of wood used in traditional Japanese sculptures by texture analysis of their low-resolution computed tomography data

Intelligent classification and analysis of regional landforms based on automatic feature selection

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