Feature Descriptor for Sri Lankan Batik Patterns Using Hu Moment Invariants and GLCM

Biometrics is the study of unique characteristics present in the human body such as fingerprint, palm-print, retina, iris, footprint, etc. While other traits have been explored widely, only a few people have been considered the foot-palm region, despite having unique properties. Prior work has explored the foot shape features using length, width, major axis, minor axis, centroid, etc. but they are not reliable for personal verification due to similarity in the physical composition of two persons. It increases the demand for more unique features based on the footprint. Footprint texture features coming from creases of foot palm are unique and permanent like palmprint texture features. Hence the main objective of the paper is to investigate various kinds of texture feature techniques. These techniques will be further used in correct extraction of footprint features. After extraction of footprint features a detailed experimental analysis is performed to discover the uniqueness in foot texture. It is further utilized to test its viability as a human recognition trait. We describe a detailed feature extraction and classification technique applied to a collected footprint data-set. For feature extraction, we use three techniques: Gray Level Co-occurrence Matrix (GLCM), Histogram Oriented Gradient (HOG), and Local Binary Patterns (LBP). Feature classification is performed using four techniques: Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Subspace Discriminant (ESD). GLCM provides less accuracy, while HOG generates a big feature vector which takes more execution time. LBP provides a trade-off between the accuracy and the execution time. Detailed quantitative experiments show: GLCM with LDA provides an accuracy of $$88.5\%$$ , HOG with Fine-KNN achieves $$86.5\%$$ accuracy and LBP with LDA achieves the accuracy of $$97.9\%$$ .

In this chapter, a content-based image retrieval technique based on the concept of region-based image retrieval has been described. This technique integrates color, texture, and shape features using local binary patterns (LBPs). In this technique, the image is divided into a fixed number of blocks and from each block LBP-based color, texture, and shape features are computed. The color and texture features are extracted using LBP histograms of quantized color image and gray-level images, respectively. Shape features are computed using the binary edge map obtained using Sobel edge detector from each block. All three features are combined to make a single completed binary region descriptor (CBRD) represented in the LBP way. To support region-based retrieval, an effective region code-based scheme is employed. In this technique, the spatial relative locations of objects are also considered to increase the retrieval accuracy.

Texture feature of the froth image is widely used in the working condition recognition of froth flotation. However, due to the complexity of the froth image, the current texture features vary greatly and are difficult to identify the work condition accurately. Therefore, we propose a dynamic texture feature named LBP on the TOP and GLCM Histograms (LTGH) which integrates the local binary patterns (LBPs) and gray-level co-occurrence matrix (GLCM) histograms on the three orthogonal planes (TOP). First, we use the rotation invariant LBPs to enhance rotation invariance and illumination robustness. Then, we implement the TOP on the enhanced texture feature map to generate the multiple dimensional enhanced feature maps. After that, we calculate the GLCM and supplementary features (SFs) on the multiple dimensional enhanced feature map. Finally, we integrate the histogram of the GLCM and SFs to discriminate the texture feature. The LTGH feature considers the froth structures both in the macrolevel and microlevel and captures the temporal information between the froth images. Experiments have demonstrated the effectiveness and stability of the proposed texture feature for work condition recognition in froth flotation. Compared with other traditional texture features, the accuracy of the LTGH feature has been increased by at least 7.76%.

Interpreting medical images is certainly a complex task which requires extensive knowledge [1]. According to Computer Aided Diagnosis (CAD) serves as a second opinion that will help radiologists in diagnosis and on the other hand Content-based Image Retrieval uses visual content to help users browse, search and retrieve similar medical images from a database based on the user’s interest [2-4]. The competency of the CBMIR system depends on feature extraction methods [5]. The textural features are very important to determine the content of a medical image. Textural features provide scenic depth, the spatial distribution of tonal variation, and surface orientation [6]. Therefore, this study seeks to compare and evaluate some of the hand-crafted texture feature extraction techniques in CBMIR. This is to help those concerned in enhancing CBIR systems to make informed decisions concerning the selection of the best textural feature extraction techniques. Since there is no clear indication of which of the various texture feature extraction techniques is best suited for a given performance metric when considering which of the techniques to choose for a particular study in CBMIR systems. The objective of this work, therefore, is to comparatively evaluate the performance of the following texture feature extraction techniques; Local Binary Pattern (LBP), Gabor Filter, Gray-Level Co-occurrence Matrix (GLCM), Haralick Descriptor, Features from Accelerated Segment Test (FAST) and Features from Accelerated Segment Test and Binary Robust Independent Elementary Features (FAST &BRIEF) using the metrics; precision, recall, f1-score, mean squared error (MSE), accuracy and time. These techniques are coupled with specific similarity measure to obtain results. The results showed that LBP, Haralick Descriptor, FAST, and GLCM had the best results in terms of (Precision and Accuracy), Time, F1-Score, and Recall respectively. LBP had 82.05% and 88.23% scores for precision and accuracy respectively. The following scores represent the performance of the Haralick descriptor, FAST, and GLCM models respectively; 0.88s, 38.7%, and 44.82%. These test scores are obtained from datasets ranging from 1k-10.5k. Aside from LBP outperforming the other 5 models mentioned, it still outperformed the following proposed models ' [7]’, ‘Tamura texture feature and wavelet transform combined with Hausdorff distance - [8]’, ‘ [9]’ in terms of (precision, accuracy, and recall) and (precision and recall) respectively and probably f1-score (since f1-score is the weighted average of precision and recall). It is believed that an ensemble of LBP, Haralick descriptors, and Support Vector Machine (SVM) can represent a robust system for both medical image retrieval and classification.

The field of machine learning is growing in modern times, computational models are able to go beyond the performance of previous forms of artificial intelligence. The use of evaluation model ,selection of model and algorithm selecting techniques play an major role in machine learning study and also in field of industries. In this work, we made evaluation of various supervised, unsupervised machine learning classifiers for flower datasets. We made use of local features such as Histogram of gradient , Kaze, Local binary pattern(LBP) ,Oriented Fast and Rotated Brief( ORB), global features like Color Histograms, Haralick Textures , Hu Moments , fusion of both and Bag of visual words(BOVW) using Vocabulary builder K-Means clustering which represents color ,texture, shape features of image. Experiment is carried out on 20 classes of flower datasets with 100 images each. .Flower datasets have many characteristic in common like sunflower will be similar to daffodil in terms of color and texture .Hence to quantify the image we need to combine different feature descriptors like color, texture and shape features. We develop a Content based classification system to find efficiency comparison of different machine learning algorithms for classification and retrieval problems. Eleven classifiers mainly Support Vector Machine, K Nearest Neighbor, Gaussian Naive Bayes , CART, Kmeans, Linear Discriminant Analysis, Adaboost ,Logistic Regression, MLP, Random Forest, CNN are analyzed on the shape, color ,texture features. Experimentation are carried out and results are recorded using CPU as well as GPU on google cobalatory platform.

Image retrieval is the process of finding images in the database that are similar to the query image by measuring how close the feature values of the query image are to other images. Image retrieval is currently dominated by approaches that combine several different representations or features. The optimal weight of each feature is needed in combining the image features such as color features, texture features, and shape features. In this study, we use a multi-layer perceptron artificial neural network (MLP) method to obtain feature weights automatically and simultaneously look for optimal weights. The color moment is used to find nine color features, Gray Level Co-occurrence Matrix (GLCM) to find four texture features, and Hu Moment to find seven shape features totaling 20 neurons and all of these features become the input layer in our MLP network. Three neurons in output layers become the automatic weight of each feature. These weights are used to combine each feature's role in obtaining the relevant image. Euclidean Distance is used to measure similarity. The average precision values obtained using automatic feature weights are 93.94% for the synthetic dataset, 91.19% for the Coil-100 dataset, and 54.31% for the Wang dataset. These results have an average difference of 5.06% with the target so automatic feature weighting works well. This value is obtained at a hidden layer size of 11 and a learning rate of 0.1. In addition, the use of automatic feature weighting gives more accurate results compared to manual feature weighting.

We established prediction models based on the combination of spectral and different advanced image features to improve the prediction accuracy of solid-soluble content (SSC) of apple. Eight optimal wavelengths were selected using a new variable selection method called variable combination population analysis (VCPA). Image textural features of the first three principal component score images were obtained using a gray level co-occurrence matrix (GLCM) and a local binary pattern (LBP). Next, a random frog algorithm was developed to select optimal textural features for further analysis. A support vector regression (SVR) model based on spectral and different textural features was developed to predict the SSC of the apple. The model based on eight optimal wavelengths and nine optimal GLCM features of principal component images yielded the best result with the determination coefficient for prediction (Rp2) of 0.9193, root mean square error for prediction (RMSEP) of 0.2955, and the ratio of the standard deviation of the prediction set to the root mean square error of prediction (RPD) with a value of 3.50. These results revealed that the spectral combined with optimal GLCM features from principal component images coupled with the SVR model has the potential for prediction of the SSC of apple.

For the demand of leaf recognition under rotation condition. This paper propose a method to extract the multi-feature extraction and Locally Preserving Projection (LPP) fusion algorithm. First, the texture feature of the leaf is extracted by Local Binary Pattern (LBP) algorithm based on leaf block. Then, in order to improve the speed of feature classification recognition, this paper adopts LPP algorithm for feature dimension reduction of the high dimensional of LBP. In addition, combining leaf texture feature LBP with shape feature (Hu Moment invariants) after dimension reduction, shaping into the comprehensive characteristics of plant leaves. Finally, Support Vector Machine (SVM) classifier is used to establish classifier for classification and identification of plant leaves, the results of experiment have verified the validity of the algorithm in this paper.

Prediction of Malignant Cell Infiltration Patterns with Texture Features of Biopsy-Correlated Positron Emission Tomography of Osteolytic Lesions in Multiple Myeloma

Plant disease is main reason of agricultural crops production losses. Leaf disease in plant occurs due to fungai, virus and bacterias. Image contains various important features which is used in classification. In this paper author initially detect disease then classify disease using extracted features. It takes five diseased leaves (Black rot, Black Measles, Leaf blight, Septoria leaf spot, Bacterial spot) and healthy leaf images then identify leaf is diseased or healthy then if leaf is diseased then classify type of disease. Color features mean, standard deviation, skewness and kurtosis computed then Region based shape feature calculated to identify the size of spots. Texture feature calculated using gray level co-occurrence matrix (GLCM) which identifies texture of image using distance and 45° angle variation in GLCM. Extracted features sent to trained feed forward neural network and classify diseased with color, shape and texture feature individually and combination of all features then observe that combination of color, shape and texture feature improve the performance of classification accuracy.

BackgroundPepper Phytophthora blight is a devastating disease during the growth process of peppers, significantly affecting their yield and quality. Accurate, rapid, and non-destructive early detection of pepper Phytophthora blight is of great importance for pepper production management. This study investigated the possibility of using multispectral imaging combined with machine learning to detect Phytophthora blight in peppers. Peppers were divided into two groups: one group was inoculated with Phytophthora blight, and the other was left untreated as a control. Multispectral images were collected at 0-h samples before inoculation and at 48, 60, 72, and 84 h after inoculation. The supporting software of the multispectral imaging system was used to extract spectral features from 19 wavelengths, and textural features were extracted using a gray-level co-occurrence matrix (GLCM) and a local binary pattern (LBP). The principal component analysis (PCA), successive projection algorithm (SPA), and genetic algorithm (GA) were used for feature selection from the extracted spectral and textural features. Two classification models were established based on effective single spectral features and significant spectral textural fusion features: a partial least squares discriminant analysis (PLS_DA) and one-dimensional convolutional neural network (1D-CNN). A two-dimensional convolutional neural network (2D-CNN) was constructed based on five principal component (PC) coefficients extracted from the spectral data using PCA, weighted, and summed with 19-channel multispectral images to create new PC images.ResultsThe results indicated that the models using PCA for feature selection exhibit relatively stable classification performance. The accuracy of PLS-DA and 1D-CNN based on single spectral features is 82.6% and 83.3%, respectively, at the 48h mark. In contrast, the accuracy of PLS-DA and 1D-CNN based on spectral texture fusion reached 85.9% and 91.3%, respectively, at the same 48h mark. The accuracy of the 2D-CNN based on 5 PC images is 82%.ConclusionsThe research indicates that Phytophthora blight infection can be detected 48 h after inoculation (36 h before visible symptoms). This study provides an effective method for the early detection of Phytophthora blight in peppers.

Gray Level Co-occurrence Matrix (GLCM), one of the best known texture analysis methods, estimates image properties related to second-order statistics. These image properties commonly known as texture features can be used for image classification, image segmentation, and remote sensing applications. In this paper, we present an FPGA based co-processor to accelerate the extraction of texture features from GLCM. Handel-C, a recently developed C-like programming language for hardware design, has been used for the FPGA implementation of GLCM texture features measurement. Results show that the FPGA has better speed performances when compared to a general purpose processor for the extraction of GLCM features.

The purpose of our study was to estimate the ability of structural MRI-based texture analysis (TA) to quantify the textural changes in corpus callosum (CC) caused by Progressive Supranuclear Palsy (PSP). Three distinct TA approaches: gray level co-occurrence matrix (GLCM), local binary pattern (LBP), oriented Gaussian derivative filter-bank (O-GDFB) were exploited and compared to present a novel study that can classify PSP ( n=16) by quantifying the textural changes in CC versus that in healthy controls (HC, n=16 using T1-W MRI. Different statistical texture features were extracted using GLCM and LBP, from the five distinct CC sub-regions: CC1 (genu), CC2 (anterior-mid), CC3 (mid-body), CC4 (posterior-mid) and CC5 (splenium). Unlike statistical based TA, a modified GDFB based TA was also performed to explore the differences in filter responses in the same CC sub-regions for different orientations and scales. Finally, texture features that showed statistically highest discrimibility were input to the SVM classifier in classifying PSP and HC. With leave-one-out cross validation, SVM classification results maximum accuracy of 88%, 94% and 84% at CC3 using GLCM, LBP and O-GDFB based TA respectively. The diagnostic potential of CC TA using structural MRI can be applied on routine radiology scan without needing any additional acquisition for quantitative analysis. Significant texture alteration found at CC3 may be of great importance in the diagnosis and understanding of this pathology.

According to changeability of cloud, cloud-type recognition was primarily based on single-class feature in previous papers which was restricted to a certain degree. A set of features describing the color, texture as well as the shape features were extracted, then the shape and texture features combination methods were discussed. Here Gray-level co-occurrence matrix(GLCM) and Gabor wavelet transform based texture features and Zernike moment based shape features were combined, then support vector machine (SVM) was employed to recognize cloud-type. Experimental results showed that the correct recognition rates of altocumulus, cirrus, clear, cumulus and stratus were improved significantly, with the average recognition rate of 88.6%, and clear sky and stratus’s recognition rate of 100%.KeywordsFeature ExtractionFeature CombinationsCloud-Type RecognitionSupport Vector Machine (SVM)

The aim of this paper is to review the current state of the art in content‐based image retrieval (CBIR), a technique for retrieving images on the basis of automatically‐derived features such as color, texture and shape. Our findings are based both on a review of the relevant literature and few results found during the work in the field. This paper focuses on the problem of texture and shape feature extractions. We investigated texture feature and shape feature for CBIR by successfully combining the Gabor filters and Zernike moments (GF+ZM). GF is used for texture feature extraction and ZM extracts shape features. For color feature, each image added to the collection is analyzed to compute a cooler histogram which shows the proportion of pixels of each color within the image. The color histogram for each image is then stored in the database.