Image segmentation by pixel classification

A fast boundary finding algorithm is presented which works without threshold operation and without any interactive control. The procedure can be described as a hierarchical two-step algorithm. In the first step the image is divided into two disjunct regions, one of them including the whole object of interest.In the second step the problem of boundary finding is suggested as a classification problem, which means that for any pixel a four-dimensional feature vector is computed which allows classification of pixels into contour elements and any other pixels.The algorithm was tested on several thousand cell images and can be easily adapted to other problems by modification of a set of parameters.     

Matrix sketching for supervised classification with imbalanced classes

Data Mining and Knowledge Discovery - The presence of imbalanced classes is more and more common in practical applications and it is known to heavily compromise the learning process. In this paper...     

Contextual classification of multispectral pixel data

Contextual statistical decision rules for classification of lattice-structured data such as pixels in multispectral imagery are developed. Their recursive implementation is shown to have a strong resemblance to relaxation algorithms. Experimental evaluation of the proposed algorithms demonstrates their effectiveness.     

Complexity measures of supervised classification problems

We studied a number of measures that characterize the difficulty of a classification problem, focusing on the geometrical complexity of the class boundary. We compared a set of real-world problems to random labelings of points and found that real problems contain structures in this measurement space that are significantly different from the random sets. Distributions of problems in this space show that there exist at least two independent factors affecting a problem's difficulty. We suggest using this space to describe a classifier's domain of competence. This can guide static and dynamic selection of classifiers for specific problems as well as subproblems formed by confinement, projection, and transformations of the feature vectors     

基于串核的音乐分类研究

给出了一种新的映射音乐到Rn空间的方法和基于串核的音乐风格分类法。首先利用统计方法分析大量音乐的旋律轮廓线得到合适的编码模式,用它把旋律轮廓线编码为有限字母表（8个字母）的字符串。利用连续子串嵌入法把音乐串显式映射到高维Rn空间,并用核表示这一映射。通过用基于核的SVM分类算法和ROC评价方法,比较了3个不同串核在5组音乐数据集上的分类性能。     

基于目标分解的极化SAR图像SVM监督分类

鉴于使用单一特征无法获得令人满意的分类效果以及SVM在小训练样本时具有良好的分类性能,提出了基于多种目标分解方法和SVM的极化SAR图像分类方法。首先对原始极化SAR图像使用多种目标分解方法进行处理,得到相应的分量信息,然后在极化SAR图像特征提取的基础上将SVM应用于极化SAR图像分类。通过选取不同的特征信息作为支持向量机的输入,比较其对分类性能的影响,得到最优的用于分类的特征信息组合,其中将相干分解和非相干分解的信息同时用做分类特征能够获得较好的分类效果。利用NASA/JPL实验室AIRSAR系统获取的全极化SAR数据进行实验处理,与Wishart监督分类进行对比,验证了将目标分解信息用做分类特征的有效性,同时与Wishart/H/α和模糊C-均值H/α分类方法进行对比,得到提出的方法具有良好的分类性能。     

Adaptable image segmentation via simple pixel classification

We propose an approach to image segmentation that views it as one of pixel classification using simple features defined over the local neighborhood. We use a support vector machine for pixel classification, making the approach automatically adaptable to a large number of image segmentation applications. Since our approach utilizes only local information for classification, both training and application of the image segmentor can be done on a distributed computing platform. This makes our approach scalable to larger images than the ones tested. This article describes the methodology in detail and tests it efficacy against 5 other comparable segmentation methods on 2 well‐known image segmentation databases. Hence, we present the results together with the analysis that support the following conclusions: (i) the approach is as effective, and often better than its studied competitors; (ii) the approach suffers from very little overfitting and hence generalizes well to unseen images; (iii) the trained image segmentation program can be run on a distributed computing environment, resulting in linear scalability characteristics. The overall message of this paper is that using a strong classifier with simple pixel‐centered features gives as good or better segmentation results than some sophisticated competitors and does so in a computationally scalable fashion.     

A relaxation method for multispectral pixel classification

Three approaches to reducing errors in multispectral pixel classification were compared: 1) postprocessing (iterated reclassification based on comparison with the neighbors' classes); 2) preprocessing (iterated smoothing, by averaging with selected neighbors, prior to classification); and 3) relaxation (probabilistic classification followed by iterative probability adjustment). In experiments using a color image of a house, the relaxation approach gave markedly superior performance; relaxation eliminated 4-8 times as many errors as the other methods did.     

Minimally supervised question classification on fine-grained taxonomies

This article presents a minimally supervised approach to question classification on fine-grained taxonomies. We have defined an algorithm that automatically obtains lists of weighted terms for each class in the taxonomy, thus identifying which terms are highly related to the classes and are highly discriminative between them. These lists have then been applied to the task of question classification. Our approach is based on the divergence of probability distributions of terms in plain text retrieved from the Web. A corpus of questions with which to train the classifier is not therefore necessary. As the system is based purely on statistical information, it does not require additional linguistic resources or tools. The experiments were performed on English questions and their Spanish translations. The results reveal that our system surpasses current supervised approaches in this task, obtaining a significant improvement in the experiments carried out.     

Color image segmentation using automatic pixel classification with support vector machine

Automatic segmentation of images is a very challenging fundamental task in computer vision and one of the most crucial steps toward image understanding. In this paper, we present a color image segmentation using automatic pixel classification with support vector machine (SVM). First, the pixel-level color feature is extracted in consideration of human visual sensitivity for color pattern variations, and the image pixel's texture feature is represented via steerable filter. Both the pixel-level color feature and texture feature are used as input of SVM model (classifier). Then, the SVM model (classifier) is trained by using fuzzy c-means clustering (FCM) with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation not only can fully take advantage of the local information of color image, but also the ability of SVM classifier. Experimental evidence shows that the proposed method has a very effective segmentation results and computational behavior, and decreases the time and increases the quality of color image segmentation in compare with the state-of-the-art segmentation methods recently proposed in the literature.     

Tweet and user validation with supervised feature ranking and rumor classification

Multimedia Tools and Applications - Filtering fake news from social network posts and detecting social network users who are responsible for generating and propagating these rumors have become two...     

Improving urban classification through fuzzy supervised classification and spectral mixture analysis

In this study, a fuzzy‐spectral mixture analysis (fuzzy‐SMA) model was developed to achieve land use/land cover fractions in urban areas with a moderate resolution remote sensing image. Differed from traditional fuzzy classification methods, in our fuzzy‐SMA model, two compulsory statistical measurements (i.e. fuzzy mean and fuzzy covariance) were derived from training samples through spectral mixture analysis (SMA), and then subsequently applied in the fuzzy supervised classification. Classification performances were evaluated between the ‘estimated’ landscape class fractions from our method and the ‘actual’ fractions generated from IKONOS data through manual interpretation with heads‐up digitizing option. Among all the sub‐pixel classification methods, fuzzy‐SMA performed the best with the smallest total_MAE (MAE, mean absolute error) (0.18) and the largest Kappa (77.33%). The classification results indicate that a combination of SMA and fuzzy logic theory is capable of identifying urban landscapes at sub‐pixel level.     

A supervised Thematic Mapper classification with a purification of training samples

Abstract

A methodology for purification of training samples for the pixel-wise Maximum Likelihood Classification is proposed. In this method, pixels which show comparatively high local spectral variability as well as spectrally separable classes are removed from the preliminary designated training samples. An example using agricultural Thematic Mapper data shows that separability can be improved 3-78 times in terms of divergence between a specific class pair; goodness of fit to Gaussian can be improved 014 times in terms of chi-square; II’9 per cent improvement of the weighted mean percentage classification accuracy can be achieved; and, most importantly, a 20-6 per cent improvement of probability of correct classification can be achieved for a specific class.     

Automatic texture feature selection for image pixel classification

Pixel-based texture classifiers and segmenters are typically based on the combination of texture feature extraction methods that belong to a single family (e.g., Gabor filters). However, combining texture methods from different families has proven to produce better classification results both quantitatively and qualitatively. Given a set of multiple texture feature extraction methods from different families, this paper presents a new texture feature selection scheme that automatically determines a reduced subset of methods whose integration produces classification results comparable to those obtained when all the available methods are integrated, but with a significantly lower computational cost. Experiments with both Brodatz and real outdoor images show that the proposed selection scheme is more advantageous than well-known general purpose feature selection algorithms applied to the same problem.     

Color texture segmentation based on image pixel classification

Image segmentation partitions an image into nonoverlapping regions, which ideally should be meaningful for a certain purpose. Thus, image segmentation plays an important role in many multimedia applications. In recent years, many image segmentation algorithms have been developed, but they are often very complex and some undesired results occur frequently. By combination of Fuzzy Support Vector Machine (FSVM) and Fuzzy C-Means (FCM), a color texture segmentation based on image pixel classification is proposed in this paper. Specifically, we first extract the pixel-level color feature and texture feature of the image via the local spatial similarity measure model and localized Fourier transform, which is used as input of FSVM model (classifier). We then train the FSVM model (classifier) by using FCM with the extracted pixel-level features. Color image segmentation can be then performed through the trained FSVM model (classifier). Compared with three other segmentation algorithms, the results show that the proposed algorithm is more effective in color image segmentation.     

Adjusted pixel features for robust facial component classification

Within the last decade increasing computing power and the scientific advancement of algorithms allowed the analysis of various aspects of human faces such as facial expression estimation [20], head pose estimation [17], person identification [2] or face model fitting [31]. Today, computer scientists can use a bunch of different techniques to approach this challenge 4, 29, 3, 17, 9 and 21. However, each of them still has to deal with non-perfect accuracy or high execution times.     

Neighbor gray levels as features in pixel classification

In segmenting an image by pixel classification, the sequence of gray levels of the pixel's neighbors can be used as a feature vector. This yields classifications at least as good as those obtained using other local properties (e.g., averages or values of difference operators) as features.     

Fuzzy ARTMAP supervised classification of multi-spectral remotely-sensed images

The fuzzy ARTMAP has been applied to the supervised classification of multi-spectral remotely-sensed images. This method is found to be more efficient, in terms of classification accuracy, compared to the conventional maximum likelihood classifier and also multi-layer perceptron with back propagation learning. The results have been discussed.