Classification Techniques for Texture Images Using Neuroph Studio

In the field of computer vision and image processing CBIR (Content based image retrieval) system become an active research area. The CBIR technique involves feature extraction based on image features (like color, shape and texture) and defining rules for comparing image. A classifier is also used by authors for image classification. For this purpose an image database taken from James Z. Wang research group website is used by many authors. Due to enormous increase in the size of database traditional methods of image retrieval by using keywords does not provide accurate result. Thus the concept of CBIR system is emerged. In last couple of years many method of feature extraction is developed but since now researcher are not able to develop a system which has gained high accuracy of human visual perception in distinguishing pictures. Therefore earlier researchers have concentrated to increase the retrieval accuracy of CBIR system by using different combination of features. The objective of this paper is to present review on a CBIR technique which combined four kinds of features set including every kind of feature for developing feature vector. Color based features (histogram), statistical features (mean, median and standard deviation), spatial features (Gabor feature and wavelet features) and texture features (GLCM) is used for forming feature vector. A SVM classifier is used with CBIR system to increase retrieval accuracy. SVM (support vector machine) is a machine learning classifier which provides good classification accuracy. Keywords: CBIR, SVM, NN, GLCM, HSV Color space, Euclidean distance matrix.

In content based image retrieval (CBIR) process, every image has been represented in a compact set of local visual features i.e. color, texture, and/or shape of images. This set of local visual features is known as feature vector. In the CBIR process, feature vectors of images have been used to represent or to identify similar images in adequate way. As a result, feature vector construction has always been considered as an important issue since it must reflect proper image semantics using minimal amount of data. The proposed CBIR scheme is based on the combination of color and texture features. In this work initially, we have converted the given RGB image into HSV color image. Subsequently, we have considered H (hue), S (saturation), and V (intensity) components for extraction of visual image features. The texture features have been extracted from the V component of the image using 2D dual-tree complex wavelet transform (2D DT-CWT) where it analyzes the textural patterns in six different directions i.e. ±15\(^{\circ }\), ±45\(^{\circ }\), and ±75\(^{\circ }\). At the same time, we have computed the probability histograms of H and S components of the image respectively and subsequently those are divided into non-uniform bins based on cumulative probability for extraction of color based features. So, in this work both the color and texture features have been extracted simultaneously. Finally, the obtained features have been concatenated to attain the final feature vector and same is considered in image retrieval process. We have tested the novelty and performance of the proposed work in two Corel, two objects, and, a texture image datasets. The experimental results reveal the acceptable retrieval performances for different types of datasets.

Texture analysis continues to evolve as a feature measurement technique to analyze and classify image data. Texture features could be derived using various approaches. One of the most important problems in the field of pattern analysis and texture classification is the problem of finding an appropriate set of features-vector that represent observations with reduced dimensionality without sacrificing discrimination power. This work aims to examines the role of principal components analysis (using PCA neural network) in achieving feature reduction in texture analysis, and attempt to draw a conclusion about the effect of feature reduction on the classification accuracy of natural-textured images (from Brodatz album) and if the feature reduction has the same effect on the behavior of different classifiers. To do this, different sets of features (statistical, statistical with radial/angular, spectral), before and after reduction, are used to train ACON based (All- Classes-One-Net) neural networks (Kohonen , LVQ, BP, PCA, and ART2) for texture-classification, then the classification accuracy of the NN with reduced and non reduced feature sets are compared and assessed.

The aim of this study was to investigate the performance of texture analysis in texture classification and tissue discrimination between MS lesions, normal appearing white matter (NAWM) and normal white matter (NWM) in order to support early diagnosis of MS. T2-weighted MR images of sixteen relapsing remitting MS (RRMS) patients and sixteen healthy subjects were selected. Based on the lesion size, sixteen regions of interests (ROIs) were chosen from MS patient MR images and healthy subject MR images for MS lesions, NAWM and NWM respectively. Texture features extracted from grey level co-occurrence matrix (GLCM) were selected based on greatest feature difference. For statistical analysis, raw data analysis (RDA), principal component analysis (PCA) and nonlinear discriminant analysis (NDA) were applied to the texture features. The k-nearest neighbor (k-NN) and artificial neural network (ANN) methods were used for texture classification. Fisher coefficient and classification accuracy were used to evaluate the performance of texture analysis. The results demonstrated that (1) classification was successful (>90.00%) between MS lesions and NAWM or NWM, less successful (88.89%) among the three tissue groups and worst (66.67%) between NAWM and NWM; (2) In statistical analysis, NDA outperforms RDA and PCA; (3) ANN classified more accurately than k-NN method between NAWM and NWM, and among the three texture types. This study demonstrated that MRI texture analysis can achieve high classification accuracy in tissue discrimination between MS lesions and NAWM or NWM, which is valuable in supporting early diagnosis of MS.

This paper proposes a new approach for content based image retrieval based on feed-forward architecture and Tetrolet transforms. The proposed method addresses the problems of accuracy and retrieval time of the retrieval system. The proposed retrieval system works in two phases: feature extraction and retrieval. The feature extraction phase extracts the texture, edge and color features in a sequence. The texture features are extracted using Tetrolet transform. This transform provides better texture analysis by considering the local geometry of the image. Edge orientation histogram is used for retrieving the edge feature while color histogram is used for extracting the color features. Further retrieval phase retrieves the images in the feed-forward manner. At each stage, the number of images for next stage is reduced by filtering out irrelevant images. The Euclidean distance is used to measure the distance between the query and database images at each stage. The experimental results on COREL- 1 K and CIFAR - 10 benchmark databases show that the proposed system performs better in terms of the accuracy and retrieval time in comparison to the state-of-the-art methods.

Development and testing of a statistical texture model for land cover classification of the Black Sea region with MODIS imagery

Detection of brain tumour is very important current scenario of the health care society. Image processing techniques are used to extract the abnormal tumour portion and other features in the brain. Brain tumor is an abnormal mass of lesson in which cells grow up and multiply uncontrollably, apparently unregulated by the mechanisms that control cells. Several techniques like Segmentation, morphological have been developed for detection of tumor in the brain. Texture is a critical feature of several image types and textural features have a lot of application in image processing, content-based image retrieval and so on. There are several ways of extracting these features and the most common way is by using a gray-level co-occurrence matrix (GLCM). In our proposed work Texture characterisation has been made to obtain the Haralick features and SVM classifier is used in the Texture classification algorithm which used in detecting the brain tumor. This technique has been tested for 45 images, true positives are 33, True negative is 1, false positive is 1, and True negatives are 10. Sensitivity 97.0%, Specificity 90.9%, Precision or Positive Predictive Value (PPV) 97.0%,Negative Predictive Value (NPV)90.9%, Accuracy 95.0%.

Abstract—This research presents an integrated framework for satellite image retrieval, segmentation, and visualization, utilizing Python-based algorithms and geospatial data processing methods. It incorporates Google Maps-based image acquisition, a Tkinter-powered interactive GUI, and the U-Net segmentation model to efficiently process satellite imagery. Satellite image segmentation, powered by U-Net, plays a vital role in geospatial analysis, enabling automated classification and extraction of key features from high-resolution imagery. The framework facilitates seamless high-resolution map generation, segmentation, and an- alytical visualization through interactive tools like bar graphs and pie charts. By leveraging U-Net’s robust architecture, this implementation enhances segmentation accuracy and supports applications in urban planning, environmental monitoring, and disaster management. Through the automation of retrieval, processing, and visualization of satellite imagery, this research advances geospatial intelligence and provides a scalable solution for efficient image segmentation workflows. Keywords—Satellite Image Segmentation, Land Cover Classi- fication, Remote Sensing, Image Processing, Semantic Segmen- tation, Deep Learning Models, Convolutional Neural Networks (CNNs), U-Net Architecture, Geospatial Analysis, Land Use Analysis

Content Based Image Retrieval (CBIR) is a process in which for a given query image similar images will be retrieved based on the image content similarity. Image content refers to its visual features, which are mathematical representations of a digital image. The image retrieval task primarily depends on image feature extraction and similarity measurement between the feature vectors. The performance of CBIR process not only decided by the optimum features extracted from the image but also on the proper choice of the similarity/dissimilarity measures (distance metrics). As the image features broadly diversified in terms of color, texture and shape based, using the same distance metric may not work well for all of these features. In this paper, first we presented overview of geometric and statistical distance metrics used in CBIR along with the comparative analysis of these measures on color and texture features. Color features extracted by computing color histograms in HSV space and texture features by wavelet decompositions. Geometrical distances such as Manhattan, Chebyshev, Euclidean, statistical distance metrics such as Cosine Similarity, Chi-square, KullbackLeibler, Jeffrey and cumulative statistical distance metrics of Kolmogorov-Smirnov, Cramer von Mises and Earthmover's distances were analyzed for feature similarity. We gave certain conclusions on the performance of all these distance metrics in terms of Mean Average Precision (MAP) and Recall rates with color and texture features respectively.

With the emerge of multimedia, communication and processing, a typical image database has a large volume of data. It is an essential to build an efficient retrieval system to browse through the entire database. In this paper we proposed precise Relevance Feed Back (RFB) Content Based Image Retrieval (CBIR) using multiple features based on interactive retrieval approach which will extensively reduces the semantic gap between low-level features and high-level semantics. Relevance Feed Back improves the retrieval accuracy of Content Based Image Retrieval(CBIR) by modifying the query based on the user's feedback in which the user can select the most relevant images and provide a weight of preference for each relevant image. In our approach we extracted multiple features by integrating color, texture and shape features with Relevance Feed Back. We extracted color features by cumulative histogram, texture feature are exploited by using Color co-occurrence matrix (CCM) and we describe the shape information contained in an image on the basis of its significant edges. We used Euclidean distance as the similarity measure. In our experiment we used Corel real-world image databases with 1000 images, divided into 10 categories, each category containing 100 images including landscapes, animals, plants, monuments, transport (cars, planes). Our results reflects that integrating color, texture and shape for retrieving similar images improves the retrieval performance with respect to retrieval accuracy and recall rate as compared with the classical color feature. During the experiment we also observed that incorporating Relevance Feed Back (RFB) with multiple features outperforms in comparison with multifeature vector without Relevance Feed Back method.

Medical image indexing and storage is gaining increased importance. Indexing and retrieval of these images efficiently is becomes an essential task. Indexing of medical images using text or numbers is a cumbersome task, difficult to memorize and time consuming. Indexing and Retrieval of images can be done through query by text and query by image which is also known as content based image retrieval (CBIR). Different medical images possess different texture and shape features. This paper presents a novel and hybrid approach of managing a huge medical image database and retrieving a medical image from the database by fusion of shape and texture features. Image is retrieved by comparing the features of query image and images of database. We attach some document information with each medical image such as patient identity, diseases, age, and case history. It can be used for further diagnostic and analysis purpose. Further the proposed algorithm also updates the database automatically if new query is found. MATLAB ® 7.01 and its image processing toolbox have been used to implement the algorithm.

Rapid development of multimedia technologies made Content Based Image Retrieval (CBIR) an energetic research area for the researchers of multimedia domain. Texture and color features have been the primal descriptors for images in the field of CBIR. This paper proposed a new CBIR system by combining the both color and texture features. Color Layout Descriptor (CLD) from MPEG-7 is used for the color feature extraction while, Mean, variance, skewness, Kurtosis, energy and entropy are used as texture descriptors. Experiments are performed on Coral Database. The results of the proposed method namely CLD-fos are compared with the four well reputed systems (i.e. SIMPLIcity, Histogram based, FIRM, and Variance Segment etc) from the industry. The results of the CLD-fos demonstrated high accuracy rate than the previous systems during the simulations. The proposed CLD-fos achieved significant performance in terms of accuracy.

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.

A number of algorithms have been developed in the past for texture analysis and classification using the wavelet transform. In this paper we explore the effect of different wavelet bases on the classification performance. We use the algorithm developed by T. Chang and C.-C. Jay Kuo (see IEEE Trans. Image Process., vol.2, no.4, p.429-41, Oct. 1993) for texture analysis and classification with a tree-structured wavelet transform. The performance of the wavelet bases is measured in terms of the sensitivity and the selectivity for the classification of natural textures. The classification results show that the choice of wavelet basis has a considerable effect on the classification performance.

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.