PCA-Based Advanced Local Octa-Directional Pattern (ALODP-PCA): A Texture Feature Descriptor for Image Retrieval

Qasim, Muhammad; Mahmood, Danish; Bibi, Asifa; Masud, Mehedi; Ahmed, Ghufran; Khan, Sameer; Jhanjhi, N. Z.; Hussain, Syed Jawad

doi:10.3390/electronics11020202

Cited by 7 publications

(4 citation statements)

References 64 publications

(89 reference statements)

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“…PCA is considered an essential process for reducing the number of features or dimensions and selecting the most important ones in the data set, thus reducing the computational time of applying classification methods in the proposed system. PCA was applied to the training chest x-ray dataset to identify the most important features or dimensions [29,30].…”

Section: Principal Component Analysismentioning

confidence: 99%

Enhanced feature selection algorithm for pneumonia detection

Abdullah¹,

Abedi²,

Hadi³

2023

PEN

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Pneumonia is a type of lung disease that can be detected using X-ray images. The analysis of chest X-ray images is an active research area in medical image analysis and computer-aided radiology. This research aims to improve the accuracy and efficiency of radiologists' work by providing a technique for identifying and categorizing diseases. More attention should be given to applying machine learning approaches to develop a robust chest X-ray image classification method. The typical method for detecting Pneumonia is through chest X-ray images but analyzing these images can be complex and requires the expertise of a radiographer. This paper demonstrates the feasibility of detecting the disease using chest X-ray images as datasets and a Support Vector Machine combined with a Naive Bayesian classifier, with PCA and GA as feature selection methods. The selected features are essential for training many classifiers. The proposed system achieved an accuracy of 92.26%, using 91% of the principal component. The study's result suggests that using PCA and GA for feature selection in chest X-ray image classification can achieve a good accuracy of 97.44%. Further research is needed to explore the use of other data mining models and care components to improve the accuracy and effectiveness of the system.

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Section: Principal Component Analysismentioning

confidence: 99%

Enhanced feature selection algorithm for pneumonia detection

Abdullah¹,

Abedi²,

Hadi³

2023

PEN

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“…Texture features derived from remote sensing imagery capture the visual homogeneity or heterogeneity within the image. These features represent specific variations in color or grayscale on the Earth's surface and are often indicative of the inherent properties of the surface objects [59][60][61]. Texture features provide image horizontal structure information and reflect the spatial variation in its gray values.…”

Section: Processing Standard Ground Survey Datamentioning

confidence: 99%

Estimation of Above-Ground Carbon Storage and Light Saturation Value in Northeastern China’s Natural Forests Using Different Spatial Regression Models

Wu,

Sun,

Jia

et al. 2023

Forests

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In recent years, accurate estimation and spatial mapping of above-ground carbon (AGC) storage in forests have been crucial for formulating carbon trading policies and promoting sustainable development strategies. Forest structure complexities mean that during their growth, trees may be affected by the surrounding environment, giving rise to spatial autocorrelation and heterogeneity in nearby forest segments. When estimating forest AGC through remote sensing, data saturation can arise in dense forest stands, adding to the uncertainties in AGC estimation. Our study used field-measured stand factors data from 138 forest fire risk plots located in Fenglin County in the Northeastern region, set within a series of temperate forest environments in 2021 and Sentinel-2 remote sensing image data with a spatial resolution of 10 m. Using ordinary least squares (OLS) as a baseline, we constructed and compared it against four spatial regression models, spatial lag model (SLM), spatial error model (SEM), spatial Durbin model (SDM), and geographically weighted regression (GWR), to better understand forest AGC spatial distribution. The results of local spatial analysis reveal significant spatial effects among plot data. The GWR model outperformed others with an R2 value of 0.695 and the lowest rRMSE at 0.273, considering spatial heterogeneity and extending the threshold range for AGC estimation. To address the challenge of light saturation during AGC estimation, we deployed traditional linear functions, the generalized additive model (GAM), and the quantile generalized additive model (QGAM). AGC light saturation values derived from QGAM most accurately reflect the actual conditions, with the forests in Fenglin County exhibiting a light saturation range of 108.832 to 129.894 Mg/ha. The GWR effectively alleviated the impact of data saturation, thereby reducing the uncertainty of AGC spatial distribution in Fenglin County. Overall, accurate predictions of large-scale forest carbon storage provide valuable guidance for forest management, forest conservation, and the promotion of sustainable development strategies.

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“…Advanced Local Octa-Directional Pattern (ALODP) based CBIR system was suggested by Qasim et al (Qasim et al, 2022). In this system, principal component analysis (PCA) based feature descriptor was implemented.…”

Section: Related Workmentioning

confidence: 99%

Content-Based Image Retrieval Using Hybrid Densenet121-Bilstm and Harris Hawks Optimization Algorithm

Sanjeevaiah¹,

Reddy²,

Karthik³

et al. 2022

International Journal of Software Innovation

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In the field of digital data management, content-based image retrieval (CBIR) has become one of the most important research areas, and it is used in many fields. This system searches a database of images to retrieve most visually comparable photos to a query image. It is based on features derived directly from the image data, rather than on keywords or annotations. Currently, deep learning approaches have demonstrated a strong interest in picture recognition, particularly in extracting information about the features of the image. Therefore, a Densenet-121 is employed in this work to extract high-level and deep characteristics from the images. Afterwards, the training images are retrieved from the dataset and compared to the query image using a Bidirectional LSTM (BiLSTM) classifier to obtain the relevant images. The investigations are conducted using a publicly available dataset named Corel, and the f-measure, recall, and precision metrics are used for performance assessment. Investigation outcomes show that the proposed technique outperforms the existing image retrieval techniques.

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PCA-Based Advanced Local Octa-Directional Pattern (ALODP-PCA): A Texture Feature Descriptor for Image Retrieval

Cited by 7 publications

References 64 publications

Enhanced feature selection algorithm for pneumonia detection

Enhanced feature selection algorithm for pneumonia detection

Estimation of Above-Ground Carbon Storage and Light Saturation Value in Northeastern China’s Natural Forests Using Different Spatial Regression Models

Content-Based Image Retrieval Using Hybrid Densenet121-Bilstm and Harris Hawks Optimization Algorithm

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