28,676 research outputs found

    SVM-based texture classification in optical coherence tomography

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    This paper describes a new method for automated texture classification for glaucoma detection using high resolution retinal Optical Coherence Tomography (OCT). OCT is a non-invasive technique that produces cross-sectional imagery of ocular tissue. Here, we exploit information from OCT im-ages, specifically the inner retinal layer thickness and speckle patterns, to detect glaucoma. The proposed method relies on support vector machines (SVM), while principal component analysis (PCA) is also employed to improve classification performance. Results show that texture features can improve classification accuracy over what is achieved using only layer thickness as existing methods currently do. Index Terms — classification, support vector machine, optical coherence tomography, texture 1

    Leaf Classification Based on GLCM Texture and SVM

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    This paper involves classification of leaves using GLCM (Gray Level Co-occurrence matrix) texture and SVM (Support Vector Machines). GLCM is used for extracting texture feature of leaves. Creating a plant database for quick and efficient classification and recognition is an important step for their conservation. This approach would help to extract useful features of leaf and improve the accuracy of leaf classification. The standard leaf images are subjected to pre-processing. Feature values are extracted from pre-processed image and they are trained and classified. Standard data sets are used for enhancing the properties of the image

    Detection of Masses in Digital Mammograms using K-means and Support Vector Machine

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    Breast cancer is a serious public health problem in several countries. Computer Aided Detection/Diagnosis systems (CAD/CADx) have been used with relative success aiding health care professionals. The goal of such systems is contribute on the specialist task aiding in the detection of different types of cancer at an early stage. This work presents a methodology for masses detection on digitized mammograms using the K-means algorithm for image segmentation and co-occurrence matrix to describe the texture of segmented structures. Classification of these structures is accomplished through Support Vector Machines, which separate them in two groups, using shape and texture descriptors: masses and non-masses. The methodology obtained 85% of accuracy

    Beat histogram features for rhythm-based musical genre classification using multiple novelty functions

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    In this paper we present beat histogram features for multiple level rhythm description and evaluate them in a musical genre classification task. Audio features pertaining to various musical content categories and their related novelty functions are extracted as a basis for the creation of beat histograms. The proposed features capture not only amplitude, but also tonal and general spectral changes in the signal, aiming to represent as much rhythmic information as possible. The most and least informative features are identified through feature selection methods and are then tested using Support Vector Machines on five genre datasets concerning classification accuracy against a baseline feature set. Results show that the presented features provide comparable classification accuracy with respect to other genre classification approaches using periodicity histograms and display a performance close to that of much more elaborate up-to-date approaches for rhythm description. The use of bar boundary annotations for the texture frames has provided an improvement for the dance-oriented Ballroom dataset. The comparably small number of descriptors and the possibility of evaluating the influence of specific signal components to the general rhythmic content encourage the further use of the method in rhythm description tasks

    A Genetic Bayesian Approach for Texture-Aided Urban Land-Use/Land-Cover Classification

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    Urban land-use/land-cover classification is entering a new era with the increased availability of high-resolution satellite imagery and new methods such as texture analysis and artificial intelligence classifiers. Recent research demonstrated exciting improvements of using fractal dimension, lacunarity, and Moran’s I in classification but the integration of these spatial metrics has seldom been investigated. Also, previous research focuses more on developing new classifiers than improving the robust, simple, and fast maximum likelihood classifier. The goal of this dissertation research is to develop a new approach that utilizes a texture vector (fractal dimension, lacunarity, and Moran’s I), combined with a new genetic Bayesian classifier, to improve urban land-use/land-cover classification accuracy. Examples of different land-use/land-covers using post-Katrina IKONOS imagery of New Orleans were demonstrated. Because previous geometric-step and arithmetic-step implementations of the triangular prism algorithm can result in significant unutilized pixels when measuring local fractal dimension, the divisor-step method was developed and found to yield more accurate estimation. In addition, a new lacunarity estimator based on the triangular prism method and the gliding-box algorithm was developed and found better than existing gray-scale estimators for classifying land-use/land-cover from IKONOS imagery. The accuracy of fractal dimension-aided classification was less sensitive to window size than lacunarity and Moran’s I. In general, the optimal window size for the texture vector-aided approach is 27x27 to 37x37 pixels (i.e., 108x108 to 148x148 meters). As expected, a texture vector-aided approach yielded 2-16% better accuracy than individual textural index-aided approach. Compared to the per-pixel maximum likelihood classification, the proposed genetic Bayesian classifier yielded 12% accuracy improvement by optimizing prior probabilities with the genetic algorithm; whereas the integrated approach with a texture vector and the genetic Bayesian classifier significantly improved classification accuracy by 17-21%. Compared to the neural network classifier and genetic algorithm-support vector machines, the genetic Bayesian classifier was slightly less accurate but more computationally efficient and required less human supervision. This research not only develops a new approach of integrating texture analysis with artificial intelligence for classification, but also reveals a promising avenue of using advanced texture analysis and classification methods to associate socioeconomic statuses with remote sensing image textures

    Texture-Based Object Recognition

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    Hlavním tématem této práce je klasifikace textur a rozpoznávání objektů v obraze podle textury. Jsou rozebírány různé texturní příznaky včetně několika variant local binary patterns (LBP). Je navržena nová modifikace LBP (weighted spatial LBP), jejímž cílem je zlepšení prostorového pokrytí tradičních LBP. Zřídka používané barevné texturní příznaky jsou také diskutovány. Umělé neuronové sítě a support vector machines jsou použity ke klasifikaci všech zmíněných příznaků. S využitím těchto metod je implementován framework pro klasifikaci textur a segmentaci obrazu. Obsažná texturní databáze je použita k testu úspěšnosti klasifikace za různých podmínek. Nakonec je systém aplikován na reálný problém - segmentaci leteckých snímků.Main subjects of this thesis are texture classification and texture-based object recognition. Various texture features are being explored, including several variants of local binary patterns (LBP). A novel modification of LBP (weighted spatial LBP) is proposed, with intention to improve on the spatial coverage of the traditional LBP. Rarely used color texture features are being discussed as well. Artificial neural networks and support vector machines are used to classify all the aforementioned features. Using these methods, framework for the texture classification and image segmentation is implemented. Comprehensive texture database is employed to test its performance under different conditions. In the end, the system is applied to solve a real-world problem - the segmentation of aerial photos.

    Texture classification of images from endoscopic capsule by using MLP and SVM – a comparative approach

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    This article reports a comparative study of Multilayer Perceptrons (MLP) and Support Vector Machines (SVM) in the classification of endoscopic capsule images. Texture information is coded by second order statistics of color image levels extracted from co-occurrence matrices. The co-occurrence matrices are computed from images rich in texture information. These images are obtained by processing the original images in the wavelet domain in order to select the most important information concerning texture description. Texture descriptors calculated from co-occurrence matrices are then modeled by using third and forth order moments in order to cope with non-Gaussianity, which appears especially in some pathological cases. Several color spaces are used, namely the most simple RGB, the most related to the human perception HSV, and the one that best separates light and color information, which uses luminance and color differences, usually known as YCbCr.Centre Algoritm

    Using local texture maps of brain MR images to detect Mild Cognitive Impairment

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    Early detection of Alzheimer's disease is expected to aid in the development and monitoring of more effective treatments. Classification methods have been proposed to distinguish Alzheimer's patients from normal controls using Magnetic Resonance Images. However, their performance drops when classifying patients at a prodromal stage, such as in Mild Cognitive Impairment. Most often, the features used in these classification tasks are related to structural measures such as volume, shape and tissue density. However, microstructural changes have been shown to arise even earlier than these larger-scale alterations. Taking this into account, we propose the use of local statistical texture maps that make no assumptions regarding the location of the affected brain regions. Each voxel contains texture information from its local neighborhood and is used as a feature in the classification of normal controls and Mild Cognitive Impairment patients. The proposed approach obtained an accuracy of 87% (sensitivity 85%, specificity 95%) with Support Vector Machines, outperforming the 63% achieved by the local gray matter density feature
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