Combination Of Texture Features Research Articles

Improving Wheat Leaf Nitrogen Concentration (LNC) Estimation across Multiple Growth Stages Using Feature Combination Indices (FCIs) from UAV Multispectral Imagery

Leaf nitrogen concentration (LNC) is a primary indicator of crop nitrogen status, closely related to the growth and development dynamics of crops. Accurate and efficient monitoring of LNC is significant for precision field crop management and enhancing crop productivity. However, the biochemical properties and canopy structure of wheat change across different growth stages, leading to variations in spectral responses that significantly impact the estimation of wheat LNC. This study aims to investigate the construction of feature combination indices (FCIs) sensitive to LNC across multiple wheat growth stages, using remote sensing data to develop an LNC estimation model that is suitable for multiple growth stages. The research employs UAV multispectral remote sensing technology to acquire canopy imagery of wheat during the early (Jointing stage and Booting stage) and late (Early filling and Late filling stages) in 2021 and 2022, extracting spectral band reflectance and texture metrics. Initially, twelve sensitive spectral feature combination indices (SFCIs) were constructed using spectral band information. Subsequently, sensitive texture feature combination indices (TFCIs) were created using texture metrics as an alternative to spectral bands. Machine learning algorithms, including partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and Gaussian process regression (GPR), were used to integrate spectral and texture information, enhancing the estimation performance of wheat LNC across growth stages. Results show that the combination of Red, Red edge, and Near-infrared bands, along with texture metrics such as Mean, Correlation, Contrast, and Dissimilarity, has significant potential for LNC estimation. The constructed SFCIs and TFCIs both enhanced the responsiveness to LNC across multiple growth stages. Additionally, a sensitive index, the Modified Vegetation Index (MVI), demonstrated significant improvement over NDVI, correcting the over-saturation concerns of NDVI in time-series analysis and displaying outstanding potential for LNC estimation. Spectral information outperforms texture information in estimation capability, and their integration, particularly with SVR, achieves the highest precision (coefficient of determination (R2) = 0.786, root mean square error (RMSE) = 0.589%, and relative prediction deviation (RPD) = 2.162). In conclusion, the sensitive FCIs developed in this study improve LNC estimation performance across multiple growth stages, enabling precise monitoring of wheat LNC. This research provides insights and technical support for the construction of sensitive indices and the precise management of nitrogen nutrition status in field crops.

Computerized diagnosis of knee osteoarthritis from x‐ray images using combined texture features: Data from the osteoarthritis initiative

AbstractThe prevalence of knee osteoarthritis (KOA) cases has witnessed a significant increase on a global scale in recent years, emphasizing the need for automated diagnostic computer systems to aid in early‐stage osteoarthritis (OA) diagnosis. The accurate characterization of knee KOA stages through feature extraction poses significant research challenges due to the complexity of identifying relevant attributes. In this study, the development of a KOA diagnostic system is presented, leveraging a combination of Gabor, and Tamura parameters using the Canonical Correlation Analysis algorithm. Two feature selection algorithms, namely Principal Component Analysis and Relief, were employed for KOA classification. Furthermore, various classifiers, including K‐Nearest Neighbors, AdaBoost, Bagging, and Random Forest, were used to assess the proposed feature extraction approach. The diagnostic system was assessed using a dataset comprising 688 x‐ray images sourced from the OA initiative (OAI) dataset, consisting of 344 images from healthy subjects (Grade 0) and 344 images from pathological patients (Grade 2). To mitigate overfitting, a 10‐fold cross‐validation method was utilized. The experimental results indicate that the combination of Tamura and Gabor parameters with the Random Forest classifier achieved remarkable performance in KOA diagnosis, yielding an accuracy of 94.59%, and an area under the curve of 98.3%. Notably, the combined Gabor and Tamura models exhibited superior performance compared to individual models, as well as existing techniques reported in the literature.

m B C C f : Multilevel Breast Cancer Classification Framework Using Radiomic Features

Breast cancer characterization remains a significant and challenging issue in contemporary medicine. Accurately distinguishing between malignant and benign breast lesions is crucial for effective diagnosis and treatment. The anatomical structure of malignant breast ultrasound images is more chaotic than that of benign images due to disease pathologies. However, texture-based analysis alone often fails to identify the extent of chaoticness in malignant breast ultrasound images due to their vague appearance with normal echo patterns, leading to missed diagnoses and increased mortality rates. To address this issue, we proposed an angular feature-based multilevel breast cancer classification framework mBCCf that aims to improve the accuracy and efficiency of classification. The proposed framework mimics the radiologist interpretation procedure by identifying the chaoticness on the periphery of the breast lesion in a breast ultrasound image (level-1). If the lesion contains an acute angle in any part of the periphery, it can be characterized as malignant or otherwise benign. However, solely relying on level-1 analysis may result in misclassification, especially when benign lesions exhibit echo patterns that resemble malignant ones. To overcome this limitation and to make the proposed system highly sensitive, advanced texture-based analysis (using combined shape, texture, and angular features) is performed (level-2). Finally, the performance of the proposed system is evaluated using a cross-dataset (consisting of 1293 breast ultrasound images) and compared with the different individual feature extraction techniques. Encouragingly, our system demonstrated an accuracy of 96.99% for classifying malignant and benign tumors, which is also validated using statistical analysis. The implications of our research lie in its potential to significantly improve breast cancer diagnosis by providing a reliable, efficient, and sensitive tool for radiologists.

Optimizing a combination of texture features with partial swarm optimizer method for bulk raisin classification

Optimizing a combination of texture features with partial swarm optimizer method for bulk raisin classification

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Image steganalysis ranges from detecting the presence of covert information in an image (passive steganalysis) to extraction of the information from the stego image (active steganalysis). One of the steps in active steganalysis is determining the stego algorithm used to produce the stego image. In this paper, a low dimensional combination of textural features is adapted for steganalysis. Also a novel blind statistical steganalyser to determine the spatial domain Least Significant Bit (LSB) based algorithms using one against one multi class classification is proposed. The proposed steganalyser is a multi class ensemble Fisher Linear Discriminant (FLD) classifier that uses novel low dimensional textural features for steganalysis. The performed experiments on the Bossbase database for 5 different LSB based algorithms for 8 different payloads show that the results are much better than the state of art steganalyser

Low Dimensional Multi Class Steganalysis of Spatial LSB based Stego Images Using Textural Features

Image steganalysis ranges from detecting the presence of covert information in an image (passive steganalysis) to extraction of the information from the stego image (active steganalysis). One of the steps in active steganalysis is determining the stego algorithm used to produce the stego image. In this paper, a low dimensional combination of textural features is adapted for steganalysis. Also a novel blind statistical steganalyser to determine the spatial domain Least Significant Bit (LSB) based algorithms using one against one multi class classification is proposed. The proposed steganalyser is a multiclass ensemble Fisher Linear Discriminant (FLD) classifier that uses novel low dimensional textural features for steganalysis. The performed experiments on the Bossbase database for 5 different LSB based algorithms for 8 different payloads show that the results are much better than the state of art steganalyser.

Identification of Myofascial Trigger Point Using the Combination of Texture Analysis in B-Mode Ultrasound with Machine Learning Classifiers.

Myofascial pain syndrome is a chronic pain disorder characterized by myofascial trigger points (MTrPs). Quantitative ultrasound (US) techniques can be used to discriminate MTrPs from healthy muscle. In this study, 90 B-mode US images of upper trapezius muscles were collected from 63 participants (left and/or right side(s)). Four texture feature approaches (individually and a combination of them) were employed that focused on identifying spots, and edges were used to explore the discrimination between the three groups: active MTrPs (n = 30), latent MTrPs (n = 30), and healthy muscle (n = 30). Machine learning (ML) and one-way analysis of variance were used to investigate the discrimination ability of the different approaches. Statistically significant results were seen in almost all examined features for each texture feature approach, but, in contrast, ML techniques struggled to produce robust discrimination. The ML techniques showed that two texture features (i.e., correlation and mean) within the combination of texture features were most important in classifying the three groups. This discrepancy between traditional statistical analysis and ML techniques prompts the need for further investigation of texture-based approaches in US for the discrimination of MTrPs.

New insights on the formation of the Jingchong Cu-Co-Pb-Zn deposit, South China: Evidence from sphalerite mineralogy and muscovite 40Ar-39Ar dating

The northeastern Hunan Province hosts numerous hydrothermal Pb-Zn(-Cu-Co) polymetallic deposits. As a representative example, the Jingchong Cu-Co-Pb-Zn deposit is characterized by the lower Cu-Co and upper Pb-Zn mineralization, whereas little is known about Pb-Zn mineralization. In this study, three generations of sphalerite were identified based on their textural and geochemical differences. The Sph-I exhibits the oscillatory zoning that consists of reddish-brown Sph-Ia (poor in chalcopyrite inclusion) alternating with dark Sph-Ib with zoned chalcopyrite inclusion. Sph-II is composed of honey-brown Sph-IIa (abundant chalcopyrite droplets) and white clean Sph-IIb (rare chalcopyrite inclusion). The black Sph-III is characterized by nano- to submicron-sized chalcopyrite inclusions with typical “dusting” or “watermelon” texture and crosscuts all other sphalerite generations in veinlets. The electron microprobe and laser ablation inductively coupled plasma mass spectrometry show that Sph-I has higher Fe and Mn contents, but lower Cd, Cu and Ag contents than Sph-II and Sph-III. Sphalerite geothermometry yields temperatures of 334–346 (±58)°C for Sph-I, 254–289 (±60)°C for Sph-II and 286 (±55) °C for Sph-III. The sulfur fugacity ranges from logfS2 values of −9.03 to −8.26 for Sph-I to −11.77 to −10.63 for Sph-II and −10.82 for Sph-III. The combined textural features and chemical compositions indicate that the self-organized mechanism forms Sph-I, and that the coupled dissolution and precipitation reactions triggered by the influx of Cu-elevated fluids are responsible for the formation of Sph-II and Sph-III. The associated pyrite and pyrrhotite inclusions in sphalerite are produced by the exsolution mechanism, while chalcopyrite inclusions are formed by co-precipitation due to local supersaturation at the interface of sphalerite with fluid. The 40Ar-39Ar dating of muscovite in the Jingchong deposit yields a mineralizing age of ca. 121.1 ± 2 Ma, consistent with the ca. 130–120 Ma Pb-Zn mineralizing events in the northeastern Hunan Province. The sulfur isotopic values (−3.0 to +3.5‰) of the Pb-Zn ores are similar to that of Cu-Co ores, indicating a magmatic sulfur origin. Together with the trace element affinity of sphalerite with magmatic-hydrothermal origin, it was proposed that the Jingchong Pb-Zn and Cu-Co mineralization were formed in the same magmatic-hydrothermal system. The placement of Pb-Zn orebodies at the upward zoning of Cu-Co orebodies could be attributed to the higher solubilities of Pb and Zn chloride complexes in hydrothermal fluids, relative to Cu chloride complex.

Abnormal neonatal brain microstructure in gestational diabetes mellitus revealed by MRI texture analysis

To investigate the value of MRI texture analysis in evaluating the effect of gestational diabetes mellitus (GDM) on neonatal brain microstructure development, we retrospectively collected images of neonates undergoing head MRI scans, including a GDM group (N1 = 37) and a healthy control group (N2 = 34). MaZda texture analysis software was used to extract the texture features from different sequence images and perform dimensionality reduction, and then the texture features selected by the lowest misjudgement rate method were imported into SPSS software for statistical analysis. In our study, we found that GDM affects the development of the microstructure of the neonatal brain, and different combinations of texture features have different recognition performances, such as different sequences and different brain regions. As a consequence, texture analysis combining multiple conventional MRI sequences has a high recognition performance in revealing the abnormal development of the brain microstructure of neonates born of mothers with GDM.

Breast Cancer Diagnosis Using Texture and Shape Features in MRI.

Contrast-enhanced magnetic resonance (MR) breast imaging represents a tool with great potential for the detection, evaluation and diagnosis of breast cancer (BC). Due to its high sensitivity and in combination with medical imaging biomarkers, it can overcome setbacks and limitations manifested in other diagnostic modalities such as mammography or ultrasound. In order to aid and assist clinicians in the diagnosis of BC, a methodology based on the extraction of 2D texture and 3D shape features in MR images is proposed. To categorize breast tumor malignancy, we considered its location in the coronal plane, divided into 4 quadrants (UOQ, UIQ, LOQ and LOQ), and the tumor type according to its genetic information (positive HER2 and Luminal B with negative HER2). In this regard, six different studies were conducted: one per feature type (texture and shape), as well as the combination of both features (texture + shape) for each of the two covariables (tumor type and location in the coronal plane). A dataset of 43 BC patients were considered. A radiomics approach was implemented extracting 43 texture and 17 shape features and using to train 5 different predictive models (Linear SVM, Gaussian SVM, Bagged Tree, KNN and Naïve Bayes). The highest precision result for the tumor type study (74.04% in terms of AUC) was obtained with 43 texture features. Whereas for the quadrant localization study, the highest precision result (67.99% AUC) was obtained as a combination of 3 textures and shape features. Both results were achieved with the SVM with Linear Kernel classification model.Clinical Relevance- This work emphasizes the use of quantitative biomarkers as texture and shape features in combination with machine learning techniques to aid in breast tumor malignancy diagnosis on MR imaging. Moreover, considering the location of the tumor in the coronal plane and its type according to its genetic information may improve the selection of appropriate treatments, survival rate, and quality of life for breast cancer patients.

Radiogenomics Analysis Linking Multiparametric MRI and Transcriptomics in Prostate Cancer.

Prostate cancer (PCa) is a highly prevalent cancer type with a heterogeneous prognosis. An accurate assessment of tumor aggressiveness can pave the way for tailored treatment strategies, potentially leading to better outcomes. While tumor aggressiveness is typically assessed based on invasive methods (e.g., biopsy), radiogenomics, combining diagnostic imaging with genomic information can help uncover aggressive (imaging) phenotypes, which in turn can provide non-invasive advice on individualized treatment regimens. In this study, we carried out a parallel analysis on both imaging and transcriptomics data in order to identify features associated with clinically significant PCa (defined as an ISUP grade ≥ 3), subsequently evaluating the correlation between them. Textural imaging features were extracted from multi-parametric MRI sequences (T2W, DWI, and DCE) and combined with DCE-derived parametric pharmacokinetic maps obtained using magnetic resonance dispersion imaging (MRDI). A transcriptomic analysis was performed to derive functional features on transcription factors (TFs), and pathway activity from RNA sequencing data, here referred to as transcriptomic features. For both the imaging and transcriptomic features, different machine learning models were separately trained and optimized to classify tumors in either clinically insignificant or significant PCa. These models were validated in an independent cohort and model performance was used to isolate a subset of relevant imaging and transcriptomic features to be further investigated. A final set of 31 imaging features was correlated to 33 transcriptomic features obtained on the same tumors. Five significant correlations (p < 0.05) were found, of which, three had moderate strength (|r| ≥ 0.5). The strongest significant correlations were seen between a perfusion-based imaging feature-MRDI A median-and the activities of the TFs STAT6 (-0.64) and TFAP2A (-0.50). A higher-order T2W textural feature was also significantly correlated to the activity of the TF STAT6 (-0.58). STAT6 plays an important role in controlling cell proliferation and migration. Loss of the AP2alpha protein expression, quantified by TFAP2A, has been strongly associated with aggressiveness and progression in PCa. According to our findings, a combination of texture features extracted from T2W and DCE, as well as perfusion-based pharmacokinetic features, can be considered for the prediction of clinically significant PCa, with the pharmacokinetic MRDI A feature being the most correlated with the underlying transcriptomic information. These results highlight a link between quantitative imaging features and the underlying transcriptomic landscape of prostate tumors.

The classification of wheat yellow rust disease based on a combination of textural and deep features.

Yellow rust is a devastating disease that causes significant losses in wheat production worldwide and significantly affects wheat quality. It can be controlled by cultivating resistant cultivars, applying fungicides, and appropriate agricultural practices. The degree of precautions depends on the extent of the disease. Therefore, it is critical to detect the disease as early as possible. The disease causes deformations in the wheat leaf texture that reveals the severity of the disease. The gray-level co-occurrence matrix(GLCM) is a conventional texture feature descriptor extracted from gray-level images. However, numerous studies in the literature attempt to incorporate texture color with GLCM features to reveal hidden patterns that exist in color channels. On the other hand, recent advances in image analysis have led to the extraction of data-representative features so-called deep features. In particular, convolutional neural networks (CNNs) have the remarkable capability of recognizing patterns and show promising results for image classification when fed with image texture. Herein, the feasibility of using a combination of textural features and deep features to determine the severity of yellow rust disease in wheat was investigated. Textural features include both gray-level and color-level information. Also, pre-trained DenseNet was employed for deep features. The dataset, so-called Yellow-Rust-19, composed of wheat leaf images, was employed. Different classification models were developed using different color spaces such as RGB, HSV, and L*a*b, and two classification methods such as SVM and KNN. The combined model named CNN-CGLCM_HSV, where HSV and SVM were employed, with an accuracy of 92.4% outperformed the other models.

Crop type classification with combined spectral, texture, and radar features of time-series Sentinel-1 and Sentinel-2 data

ABSTRACT Crop type mapping visualizes the spatial distribution pattern and proportion of planting areas of different crop types, which is the basis for subsequent agricultural applications. Although optical remote sensing has been widely used to monitor crop dynamics, data are not always available due to cloud and other atmospheric effects on optical sensors. Satellite microwave systems such as Synthetic Aperture Radar (SAR) have all-time and all-weather advantages in monitoring ground and crop conditions, combining optical imagery and SAR imagery for crop type classification is of great significance. Our study mainly proposes seven feature combination schemes based on the combination of multi-temporal spectral features and texture features of Sentinel-2 (S2), and radar backscattering features of Sentinel-1 (S1) evaluate the influence of different data sources and different features on classification accuracy, obtains the optimal classification strategy and analyses the contribution of different features to classification result, in the aim of providing a new technical approach for the fine identification of crops from multi-source remote-sensing data. Results show that the crop classification accuracy of combined multi-time series spectral, texture, and radar features is higher than that of combining two types of features. The features subset selected by multi-period spectral, texture, and radar features have the best classification result, the overall accuracy (OA) and kappa coefficients reach 96.40% and 0.93, respectively. The study provides a method reference for future research on larger-scale remote-sensing crop precise extraction.

IUNet-IF: identification of construction waste using unmanned aerial vehicle remote sensing and multi-layer deep learning methods

ABSTRACT Considering current problems in the identifying, locating, and supervising of construction waste, it is difficult to achieve real-time target judgement and identification in the short term. This study proposes a deep learning-based construction waste identification and classification system, named IUNet-IF, developed using unmanned aerial vehicle (UAV) images. High-resolution UAV aerial images were used to establish a sample database and train U-Net models. The U-Net model, which exhibited the highest overall performance and identification accuracy, was enhanced to develop an IUNet model. Subsequently, the texture and colour features of UAV images were extracted and input to the IUNet model to build a novel IUNet-IF model. The proposed method uses the optimal model and an input combination of texture features, colour features, and original images, which improves the segmentation results compared to those when only the original image is used. The experimental results indicate that the system can achieve a maximum pixel accuracy index, mean intersection over union (mIoU), and mean pixel accuracy (MPA) of 98.98%, 93.78%, and 97.26%, respectively. These results verify the feasibility of extracting construction waste covered by a dust-proof net from UAV remote sensing images and can provide reference information for the automatic extraction of construction waste.

Estimating soil salinity using Gaofen-2 imagery: A novel application of combined spectral and textural features

Gaofen-2 (GF-2) imagery data has been playing an important role in environmental monitoring. However, the scarcity of spectral bands makes GF-2 difficult to use in soil salinity estimation. In this paper, we combined spectral and textual features for soil salinity estimation from GF-2 imagery. The spectral features comprised five classes of predictors: spectral value, vegetation index, salinity index, brightness index, and intensity index. Four gray-level co-occurrence matrix (GLCM) indices were used as the textural features. The least absolute shrinkage and selection operator (LASSO) was applied to select features. Four methods, namely, Random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and partial least squares regression (PLSR) were applied and compared. To this end, 211 soil samples were collected in the Yellow River Delta through field investigation. The results showed that GF-2 imagery could successfully estimate soil salinity by integrating spectral and texture features, and among the four methods, the RF had the highest accuracy with the determination coefficient for cross-validation (R2CV), a root mean square error for cross-validation (RMSECV), and the ratio of the standard deviation to the root mean square error of prediction (RPD) of 0.82, 2.36 g kg−1, and 2.28, respectively. Especially, the impact of different scale features on the soil salinity estimation accuracy was evaluated. The optimal window size for features was 9 × 9 pixels, and increasing or decreasing the window size will decrease the estimation accuracy. The study provides a novel application to soil salinity estimation from remote sensing imagery.

Multiclass tumor identification using combined texture and statistical features.

Early detection and diagnosis of brain tumors are essential for early intervention and eventually successful treatment plans leading to either a full recovery or an increase in the patient lifespan. However, diagnosis of brain tumors is not an easy task since it requires highly skilled professionals, making this procedure both costly and time-consuming. The diagnosis process relying on MR images gets even harder in the presence of similar objects in terms of their density, size, and shape. No matter how skilled professionals are, their task is still prone to human error. The main aim of this work is to propose a system that can automatically classify and diagnose glioma brain tumors into one of the four tumor types: (1) necrosis, (2) edema, (3) enhancing, and (4) non-enhancing. In this paper, we propose a combined texture discrete wavelet transform (DWT) and statistical features based on the first- and second-order features for the accurate classification and diagnosis of multiclass glioma tumors. Four well-known classifiers, namely, support vector machines (SVM), random forest (RF), multilayer perceptron (MLP), and naïve Bayes (NB), are used for classification. The BraTS 2018 dataset is used for the experiments, and with the combined DWT and statistical features, the RF classifier achieved the highest average accuracy whether for separated modalities or combined modalities. The highest average accuracy of 89.59% and 90.28% for HGG and LGG, respectively, was reported in this paper. It has also been observed that the proposed method outperforms similar existing methods reported in the extant literature.

Novel hybrid approach based on combination of textural features and clinical parameters for reliable prediction of thyroid malignancy

The dramatic increase in thyroid cancer, particularly among the younger population demands development of an automated decision support system for timely and reliable prognosis of the disease so as to facilitate improved chances of recovery in the subjects. While numerous methods are already reported by the researchers for the detection of Thyroid malignancy, the most crucial parameter of Thyroid Malignancy Index (TMI) has received very less attention. TMI is of paramount importance in diagnosis and treatment of the patients having malignant thyroid and its consideration is therefore inevitable. This research aims to develop an automated and a reliable decision support system for detection of thyroid malignancy. The proposed hybrid approach incorporates a novel combination of texture features and clinically observable parameters to initially identify a malignant thyroid tumor using support vector machines and further predicts its TMI value, thus exhibiting a performance like a trained radiologist. Publically available database comprising of 99 cases and 134 ultrasound images are used to validate the superiority of the proposed approach. Apt consideration and reliable prediction of the TMI in this research makes the designed approach novel and marks a mega leap towards its practical deployment in the clinical environment.

Classification of forest types in Cuigang Forest Farm based on time series data of Landsat 8

To explore the practical role of enhanced vegetation index (EVI) time series data in improving the accuracy of forest type recognition could promote the deep application of optical remote sensing data in forest resources investigation and monitoring. With Cuigang Forest Farm of Xinlin Forestry Bureau in Daxing'anling as the object, we constructed six classification schemes, using random forest algorithm with spectral feature, texture feature and EVI time series feature. The data sources were 20-view Landsat 8 OLI time series data from 2014 to 2018, 56 fixed plots data from 2017-2019, and the 2016 Class II survey data. Our aims were to realize the classification of forest types in Cuigang Forest Farm and to evaluate the accuracy of different classification schemes. The results showed the EVI values of Larix gmelinii forest, Betula platyphylla forest, coniferous-broadleaved mixed forest, coniferous mixed forest and broadleaved mixed forest were significantly different in non-growing seasons (36-111 days and 287-367 days), with the EVI value of mixed conifer forest being significantly higher, and that of mixed broadleaf forest being always lower than the other four forest types. In the early growing season (111-143 days), the EVI value of B. platyphylla forest were higher than L. gmelinii forest, which could effectively distinguish the two forests. Among the six classification schemes, spectral feature, texture feature, and EVI time series feature had the highest classification accuracy, with a Kappa of 0.82 and a classification accuracy of 86.1%. The comparison results showed that the overall accuracy of adding vegetation index time series feature was improved by 14.3% compared with that of spectral feature. The random forest algorithm with combined spectral, texture and EVI time series features could effectively classify forest stand types in Cuigang Forest Farm, with good recognition accuracy and confidence.

Crack Texture Feature Identification of Fiber Reinforced Concrete Based on Deep Learning.

Structural cracks in concrete have a significant influence on structural safety, so it is necessary to detect and monitor concrete cracks. Deep learning is a powerful tool for detecting cracks in concrete structures. However, it requires a large quantity of training samples and is costly in terms of computational time. In order to solve these difficulties, a deep learning target detection framework combining texture features with concrete crack data is proposed. Texture features and pre-processed concrete data are merged to increase the number of feature channels in order to reduce the demand of training samples for the model and improve training speed. With this framework, concrete crack detection can be realized even with a limited number of samples. To accomplish this aim, self-made steel fiber reinforced concrete crack data is used for comparison between our framework and those without texture feature mergence or pre-processed concrete data. The experimental results show that the number of parameters that need to be fitted in the model training and training time can be correspondingly reduced and the detection accuracy can also be improved.

Content-Based Image Retrieval Using Colour, Gray, Advanced Texture, Shape Features, and Random Forest Classifier with Optimized Particle Swarm Optimization.

In this paper, a new approach for Content-Based Image Retrieval (CBIR) has been addressed by extracting colour, gray, advanced texture, and shape features for input query images. Contour-based shape feature extraction methods and image moment extraction techniques are used to extract the shape features and shape invariant features. The informative features are selected from extracted features and combined colour, gray, texture, and shape features by using PSO. The target image has been retrieved for the given query image by training the random forest classifier. The proposed colour, gray, advanced texture, shape feature, and random forest classifier with optimized PSO (CGATSFRFOPSO) provide efficient retrieval of images in a large-scale database. The main objective of this research work is to improve the efficiency and effectiveness of the CBIR system by extracting the features like colour, gray, texture, and shape from database images and query images. These extracted features are processed in various levels like removing redundancy by optimal feature selection and fusion by optimal weighted linear combination. The Particle Swarm Optimization algorithm is used for selecting the informative features from gray and colour and texture features. The matching accuracy and the speed of image retrieval are improved by an ensemble of machine learning algorithms for the similarity search.