GLCM Features Research Articles

PLANT RECOGNITION USING STEREO LEAF IMAGE USING GRAY-LEVEL CO-OCCURRENCE MATRIX

Adequate knowledge, such as information about the unique characteristics of each plant, is necessary to identify plant. Researchers have made plant recogni tion based on leaf characteristics. The leaf image- based plant recognition in view of different angles is a new challenge. In this study, the research on the p lant recognition was conducted based on leaf images resulted from 3D stereo camera. The 3D images are very influential in the development of computer vision t heory, which can provide more detailed information of an object. One of the information that can be obtai ned is about the position of the object in its imag e with the background as well as of the camera. One of the ways used to obtain such information is to calcula te the disparity. However, this method will only tell the position of the object compared to other objects wi thout that of range. Sum Absolute Different (SAD) is a method that can be used to find the disparity value. The SAD method does not require heavy computations and long process. Before calculating the disparity, all the images should be previously segmented. The objective of this segmentation is to separate all the objects from the background. Furthermore, filtering and polynomial transformation at the results of disparity is necess ary to improve the quality of resultant images. Furthermor e, 22 features were extracted using GLCM features (second order statistics) of images resulted from disparity improvement. The highest accuracy of match in the recognition of plant varieties was obtained at 50 cm distance a nd in the recognition of three plant varieties was 83.3%.

FUZZY BASED IMAGE DIMENSIONALITY REDUCTION USING SHAPE PRIMITIVES FOR EFFICIENT FACE RECOGNITION

Today face recognition capability of the human visual system plays a significant role in day to day life due to numerous important applications for automatic face recognition. One of the problems with the recent image classification and recognition approaches are they have to extract features on the entire image and on the large grey level range of the image. The present paper overcomes this by deriving an approach that reduces the dimensionality of the image using Shape primitives and reducing the grey level range by using a fuzzy logic while preserving the significant attributes of the texture. The present paper proposed an Image Dimensionality Reduction using shape Primitives (IDRSP) model for efficient face recognition. Fuzzy logic is applied on IDRSP facial model to reduce the grey level range from 0 to 4. This makes the proposed fuzzy based IDRSP (FIDRSP) model suitable to Grey level co-occurrence matrices. The proposed FIDRSP model with GLCM features are compared with existing face recognition algorithm. The results indicate the efficacy of the proposed method.

Osteoarthritis classification using self organizing map based on gabor kernel and contrast-limited adaptive histogram equalization.

Localization is the first step in osteoarthritis (OA) classification. Manual classification, however, is time-consuming, tedious, and expensive. The proposed system is designed as decision support system for medical doctors to classify the severity of knee OA. A method has been proposed here to localize a joint space area for OA and then classify it in 4 steps to classify OA into KL-Grade 0, KL-Grade 1, KL-Grade 2, KL-Grade 3 and KL-Grade 4, which are preprocessing, segmentation, feature extraction, and classification. In this proposed system, right and left knee detection was performed by employing the Contrast-Limited Adaptive Histogram Equalization (CLAHE) and the template matching. The Gabor kernel, row sum graph and moment methods were used to localize the junction space area of knee. CLAHE is used for preprocessing step, i.e.to normalize the varied intensities. The segmentation process was conducted using the Gabor kernel, template matching, row sum graph and gray level center of mass method. Here GLCM (contrast, correlation, energy, and homogeinity) features were employed as training data. Overall, 50 data were evaluated for training and 258 data for testing. Experimental results showed the best performance by using gabor kernel with parameters α=8, θ=0, Ψ=[0 π/2], γ=0,8, N=4 and with number of iterations being 5000, momentum value 0.5 and α0=0.6 for the classification process. The run gave classification accuracy rate of 93.8% for KL-Grade 0, 70% for KL-Grade 1, 4% for KL-Grade 2, 10% for KL-Grade 3 and 88.9% for KL-Grade 4.

Classification of Satellite Images by Texture-Based Models Modulation Using MLP, SVM Neural Networks and Nero Fuzzy

The purpose of this paper is the automatic identification of various districts in satellite images using the textural feature, while comparing them by two methods of GLCM and Fourier Spectrum. The modulation of discrete violet and GLCM yielded a new method for the identification of the urban areas that is used as a criterion for measuring the development rate in the urban areas using satellite images. Through the modulation of GLCM and spatial features, MLP Neural Network and saliency measurements made it possible to determine the most important textural features for sub-metric spatial resolution imagery of urban scenes. That is used as a criterion for measuring the development rate in the urban areas using satellite images. The results of simulation using MATLAB/IMAGE PROCESSING software on IKONOS database, from which the images have been collected, verify the accuracy of the performance of this system. 

Texture-based features for classification of mammograms using decision tree

Mammogram—breast X-ray—is considered the most effective, low cost, and reliable method in early detection of breast cancer. Although general rules for the differentiation between benign and malignant breast lesions exist, only 15–30 % of masses referred for surgical biopsy are actually malignant. In this work, an approach is proposed to develop a computer-aided classification system for cancer detection from digital mammograms. The proposed system consists of three major steps. The first step is region of interest (ROI) extraction of 256 × 256 pixels size. The second step is the feature extraction; we used a set of 19 GLCM and GLRLM features, and the 19 (nineteen) features extracted from gray-level run-length matrix and gray-level co-occurrence matrix could distinguish malignant masses from benign masses with an accuracy of 96.7 %. Further analysis was carried out by involving only 12 of the 19 features extracted, which consists of 5 features extracted from GLCM matrix and 7 features extracted from GLRL matrix. The 12 selected features are as follows: Energy, Inertia, Entropy, Maxprob, Inverse, SRE, LRE, GLN, RLN, LGRE, HGRE, and SRLGE; ARM with 12 features as prediction can distinguish malignant mass image and benign mass with a level of accuracy of 93.6 %. Further analysis showed that area under the receiver operating curve was 0.995, which means that the accuracy level of classification is good or very good. Based on that data, it was concluded that texture analysis based on GLCM and GLRLM could distinguish malignant image and benign image with considerably good result. The third step is the classification process; we used the technique of decision tree using image content to classify between normal and cancerous masses. The proposed system was shown to have the large potential for cancer detection from digital mammograms.

ANN and SVM Based War Scene Classification Using Invariant Moments and GLCM Features: A Comparative Study

ANN and SVM Based War Scene Classification Using Invariant Moments and GLCM Features: A Comparative Study

Efficient Image Mining Technique for Classification of Mammograms to Detect Breast Cancer

The image mining technique deals with the extraction of implicit knowledge and image with data relationship or other patterns not explicitly stored in the images. It is an extension of data mining to image domain. The main objective of this paper is to apply image mining in the domain such as breast mammograms to classify and detect the cancerous tissue. Mammogram image can be classified into normal, benign and malignant class. Total of 24 features including histogram intensity features and GLCM features are extracted from mammogram images. A hybrid approach of feature selection is proposed which approximately reduces 75% of the features and new decision tree is used for classification. Experiments have been taken for a data set of 300 images taken from MIAS of different types with the aim of improving the accuracy by generating minimum no. of rules to cover more patterns.