基于概念的形状分类与识别

基于内容的图像检索已经成为图像检索的一个重要研究方向。其中利用形状特征可以实现形状较为明显的各类图像库的检索。该文对图像形状特征的提取和描述方法进行了较深入地研究,选取其中一部分特征值,分别利用BP神经网络和支持向量机进行形状识别,取得令人满意的实验效果。     

支持向量机序贯最小优化算法推导的改进

已有文献中的支持向量机SMO算法推导过程计算复杂,该文给出一个简洁推导。整个推导过程没有复杂的计算,除了误差函数外,不需引入其它中间变量。     

Identification of EMG signals using discriminant analysis and SVM classifier

The electromyography (EMG) signal is a bioelectrical signal variation, generated in muscles during voluntary or involuntary muscle activities. The muscle activities such as contraction or relaxation are always controlled by the nervous system. The EMG signal is a complicated biomedical signal due to anatomical/physiological properties of the muscles and its noisy environment. In this paper, a classification technique is proposed to classify signals required for a prosperous arm prosthesis control by using surface EMG signals. This work uses recorded EMG signals generated by biceps and triceps muscles for four different movements. Each signal has one single pattern and it is essential to separate and classify these patterns properly. Discriminant analysis and support vector machine (SVM) classifier have been used to classify four different arm movement signals. Prior to classification, proper feature vectors are derived from the signal. The feature vectors are generated by using mean absolute value (MAV). These feature vectors are provided as inputs to the identification/classification system. Discriminant analysis using five different approaches, classification accuracy rates achieved from very good (98%) to poor (96%) by using 10-fold cross validation. SVM classifier gives a very good average accuracy rate (99%) for four movements with the classification error rate 1%. Correct classification rates of the applied techniques are very high which can be used to classify EMG signals for prosperous arm prosthesis control studies.     

层次分析与支持向量机组合评价方法的研究与应用

充分发挥层次分析法与支持向量机各自的优势,将两种方法有机结合起来并综合运用于学生创新能力的评价。研究结果表明,这种组合方法是可行和有效的,可很好地应用于高等学校学生的评价工作。     

基于支持向量机的心律失常诊断研究

运用计算机技术辅助激光治疗心律失常疾病,通过非线性动力学分析心电信号,采用模式识别工具实现心律失常疾病的初步诊断。首先提出一种基于经验模式分解（EMD）的时间尺度滤波法,从而有效的抑制心电信号噪声。然后提取预处理后数据的排列熵（Permutatior Entropy,PE）特征值。最后利用支持向量机（SVM）分类器验证特征值的分类。实验结果表明该算法能有效的实现正常和异常心电信号的自动分类,从而简便快速的初步诊断心律失常疾病。     

Learning of perceptual grouping for object segmentation on RGB-D data

Object segmentation of unknown objects with arbitrary shape in cluttered scenes is an ambitious goal in computer vision and became a great impulse with the introduction of cheap and powerful RGB-D sensors. We introduce a framework for segmenting RGB-D images where data is processed in a hierarchical fashion. After pre-clustering on pixel level parametric surface patches are estimated. Different relations between patch-pairs are calculated, which we derive from perceptual grouping principles, and support vector machine classification is employed to learn Perceptual Grouping. Finally, we show that object hypotheses generation with Graph-Cut finds a globally optimal solution and prevents wrong grouping. Our framework is able to segment objects, even if they are stacked or jumbled in cluttered scenes. We also tackle the problem of segmenting objects when they are partially occluded. The work is evaluated on publicly available object segmentation databases and also compared with state-of-the-art work of object segmentation.     

一种改进的支持向量机模式分类方法

支持向量机(Support Vector Machine,SVM)是在统计学习理论基础上发展起来的一种新的机器学习方法,已成为目前研究的热点,并在模式识别领域有了广泛的应用.首先分析了支持向量机原理,随后引入一种改进的径向基核函数,在此基础上,提出了一种改进核函数的SVM模式分类方法.与基于IRIS数据,进行了计算机仿真实验,与基干模糊k-近邻的模式分类仿真结果比较,结果表明改进的SVM方法分类性能比模糊k-近邻算法(Fuzzy k-Nearest Neighbor,FKNN)的分类性能更好,运算时间更短,更易于实时实现.     

用支持向量机为学习过程建模

在游戏开发的人工智能领域中,最关键的部分是学习过程．经典的学习系统需要花费很多时间忘记旧规则,同时学习新规则．为了解决这个问题,文中提出使用支持向量机这种技术,并为这种学习分类器引进了一个时间概念,这样,系统就可以很容易地忘记过去的事情．同时也为支持向量的数量限制和CPU消耗的限制提出了解决方法．．     

Adaptive Kernel Function of SVM for Improving Speech/Music Classification of 3GPP2 SMV

Because a wide variety of multimedia services are provided through personal wireless communication devices, the demand for efficient bandwidth utilization becomes stronger. This demand naturally results in the introduction of the variable bitrate speech coding concept. One exemplary work is the selectable mode vocoder (SMV) that supports speech/music classification. However, because it has severe limitations in its classification performance, a couple of works to improve speech/music classification by introducing support vector machines (SVMs) have been proposed. While these approaches significantly improved classification accuracy, they did not consider correlations commonly found in speech and music frames. In this paper, we propose a novel and orthogonal approach to improve the speech/music classification of SMV codec by adaptively tuning SVMs based on interframe correlations. According to the experimental results, the proposed algorithm yields improved results in classifying speech and music within the SMV framework.     

图像语义提取方法研究

为解决从图像的低层视觉特征到高层语义特征的＂语义鸿沟＂问题,对当前的语义提取方法进行研究,简单介绍了图像语义层次模型,并根据语义信息的来源不同,归纳总结了图像语义中基于处理范围的方法,基于机器学习的方法,基于人机交互的方法和基于外部信息源的提取方法,这些工作为图像语义提取和图像语义检索等研究提供有益参考。