Semantic modeling of natural scenes based on contextual Bayesian networks

This paper presents a novel approach based on contextual Bayesian networks (CBN) for natural scene modeling and classification. The structure of the CBN is derived based on domain knowledge, and parameters are learned from training images. For test images, the hybrid streams of semantic features of image content and spatial information are piped into the CBN-based inference engine, which is capable of incorporating domain knowledge as well as dealing with a number of input evidences, producing the category labels of the entire image. We demonstrate the promise of this approach for natural scene classification, comparing it with several state-of-art approaches.     

Salient feature and reliable classifier selection for facial expression classification

A novel facial expression classification (FEC) method is presented and evaluated. The classification process is decomposed into multiple two-class classification problems, a choice that is analytically justified, and unique sets of features are extracted for each classification problem. Specifically, for each two-class problem, an iterative feature selection process that utilizes a class separability measure is employed to create salient feature vectors (SFVs), where each SFV is composed of a selected feature subset. Subsequently, two-class discriminant analysis is applied on the SFVs to produce salient discriminant hyper-planes (SDHs), which are used to train the corresponding two-class classifiers. To properly integrate the two-class classification results and produce the FEC decision, a computationally efficient and fast classification scheme is developed. During each step of this scheme, the most reliable classifier is identified and utilized, thus, a more accurate final classification decision is produced. The JAFFE and the MMI databases are used to evaluate the performance of the proposed salient-feature-and-reliable-classifier selection (SFRCS) methodology. Classification rates of 96.71% and 93.61% are achieved under the leave-one-sample-out evaluation strategy, and 85.92% under the leave-one-subject-out evaluation strategy.     

An improved method for voice pathology detection by means of a HMM-based feature space transformation

This paper presents new a feature transformation technique applied to improve the screening accuracy for the automatic detection of pathological voices. The statistical transformation is based on Hidden Markov Models, obtaining a transformation and classification stage simultaneously and adjusting the parameters of the model with a criterion that minimizes the classification error. The original feature vectors are built up using classic short-term noise parameters and mel-frequency cepstral coefficients. With respect to conventional approaches found in the literature of automatic detection of pathological voices, the proposed feature space transformation technique demonstrates a significant improvement of the performance with no addition of new features to the original input space. In view of the results, it is expected that this technique could provide good results in other areas such as speaker verification and/or identification.     

Automated breast cancer detection and classification using ultrasound images: A survey

Breast cancer is the second leading cause of death for women all over the world. Since the cause of the disease remains unknown, early detection and diagnosis is the key for breast cancer control, and it can increase the success of treatment, save lives and reduce cost. Ultrasound imaging is one of the most frequently used diagnosis tools to detect and classify abnormalities of the breast. In order to eliminate the operator dependency and improve the diagnostic accuracy, computer-aided diagnosis (CAD) system is a valuable and beneficial means for breast cancer detection and classification. Generally, a CAD system consists of four stages: preprocessing, segmentation, feature extraction and selection, and classification. In this paper, the approaches used in these stages are summarized and their advantages and disadvantages are discussed. The performance evaluation of CAD system is investigated as well.     

Spatial and temporal variability of macrophyte cover and productivity in the eastern Amazon floodplain: A remote sensing approach

Herbaceous aquatic macrophytes cover extensive areas on the floodplains of the Amazon basin and are an important habitat and input of organic carbon. These communities have large intra- and inter-annual variability, and characterization of this variability is necessary to quantify the role of macrophytes in the ecology and biogeochemistry of the floodplain. A novel approach for mapping the temporal evolution of aquatic vegetation in the Amazon floodplain, which could be adapted to other spatially and temporally changing environments, is presented. Macrophyte cover varied seasonally and inter-annually, ranging between 104 and 198 km² for the floodplain examined (total area, 984 km²). The observed evolution of plant distribution indicated a spatial and temporal partition of macrophyte communities into short-lived and annual groups. A simulation of macrophyte net primary production (NPP) based on the mapping results indicated that at least 3% of NPP could be attributed to the short-lived communities. The present results suggest that significant changes in the macrophyte's contribution to carbon cycling in the Amazon floodplain could occur as a result of the predicted increase in frequency of drought years for the Amazon system due to climate change.     

基于RBF神经网络的Web文本分类的研究

Web文本分类是采用文本分类技术将Web上的信息进行自动分类,使用户能够快速找到自己想要的资源。文本分类的过程中,将特征提取之后的来自Web的数据分成样本数据集和测试数据集,将样本数据集输入到RBF网络中进行训练,RBF网络经过训练之后,输入测试数据集中的数据进行验证,实验证明,RBF网络取得了较好的分类结果。     

特征权重优化高分辨率遥感影像模糊分类研究

在针对SPOT5等高分辨率遥感影像的面向对象模糊分类过程中,一般对影像对象的特征赋予相同的权重。为了体现不同特征对分类作用的差异,本文在分类时根据特征的重要与否,对参与分类的特征赋予不同的权重,提高重要的、区分度好的特征的权重,降低次要特征的权重。以北京市昌平区的SPOT5影像为例,利用多特征模糊分类和经过权重优化的多特征模糊分类进行分类对比实验。实验结果表明,经过特征权重优化的分类与权重相同的分类结果相比,分类总精度由原来的86.3%提高到了92.6%,Kappa系数由原来的0.8096提高到了0.8947。结果表明,经过权重优化的多特征模糊分类有助于提高模糊分类法的分类精度和适用性。     

水浇地与旱地分类的研究进展

水浇地和旱地的分类研究对于粮食安全、估算农业灌溉需水量以及完善耕地二级类型的分类有着重要的作用。本文从水浇地和旱地分类的意义、分类方法以及分类应用的现状三方面,对国内外水浇地和旱地的分类研究进展及其特点进行了归纳总结,得出了三点结论:(1)参与水浇地和旱地分类的数据源主要为中低分辨率的时间序列植被指数产品以及一些辅助信息;(2)水浇地和旱地的分类特征主要为时间序列的植被指数以及一些辅助特征;(3)水浇地和旱地的分类方法主要为数字化、非监督分类和监督分类。同时指出,丰富水浇地和旱地的分类特征、引入智能型分类方法以及探索生态环境背景在水浇地和旱地分类中的应用将成为未来水浇地和旱地分类研究的重要内容。     

支持向量机在文本特征提取中的应用研究

在文本自动分类中,针对如何进行文本特征的选择和提取这一关键和基础性工作,提出用支持向量度量词汇对分类的贡献,然后进行文本特征的提取。实验结果表明,该方法可以在确保分类信息不损失的前提下,降低向量空间的维数,提高分类器效率和分类准确率。     

面向股票新闻的情感分类方法

互联网新闻资讯对证券市场和投资者有举足轻重的影响,新闻进行情感分类后再展示给用户,可以帮助投资者迅速做出投资决定.从文本分类的基本方法出发,实现了基于N-gram 统计模型的新词发现方法,并将所得结果用于构建中文分词词典和情感词典.同时引入评价理论,并用朴素贝叶斯、K 近邻和支持向量机3 种方法进行股票新闻标题的情感分类实验.所用实验数据来自2009 年“新浪财经”共计23 万余条的新闻标题,结果表明二分类的准确率最高可达82.9%.此外,还实现了一个原型系统用于展示股票新闻的分类结果.