Image Co-labeling Based on Active Learning

图像的搜集与标记是计算机视觉研究的起点，一个标记好的图像集对分类器的学习及评估起着重要的作用。然而，现有的图像集及其图像标记基本上都是通过手工完成，费时耗力，不利于大规模图像集的建立。因此，研究图像自动标记关键技术对计算机视觉，模式识别等领域具有重要的科学价值。 本文研究了在已知少量标记样本的条件下，对大量没标记的图像进行标记的问题，提出了一种新颖的图像自动标记算法，该算法在协同学习的框架下，以主动学习的方式，采用两种特征互补的分类器对目标进行标记。主要的研究工作和创新点如下： 1.提出对图像标记采用协同标记方式的算法。该算法采用HOG和LBP两种互补的特征形成两个分类器，对未标记图像进行...Large databases of labeled images are the beginning of computer vision research; a labeled dataset plays an important role in learning and evaluating the classifier. However, the collection of image database and labeling images are basically done by hand currently, and it is trouble, labor-intensive and not conducive to establish a large image database. Therefore, the key technologies of image aut...学位：工学硕士院系专业：信息科学与技术学院计算机科学系_计算机应用技术学号：2302007115128

Traffic Sign Detection Based on Color Segmentation and Multi-features Fusion

采用了一种鲁棒的交通标志检测算法,该算法结合了基于颜色分割的粗定位过程和基于多特征融合的交通标志精确定位过程.粗定位利用交通标志的颜色特征,采用基于yIQ空间的颜色分割方法,获得图像中有可能包含交通标志的图像子区域;基于多特征融合的精确定位是采用梯度方向直方图(HISTOgrAM Of OrIEnTEd grAdIEnT,HOg)及局域二值模式(lOCAl bInAry PATTErn,lbP)两种互补的特征,并利用支持向量机(SuPPOrT VECTOr MACHInC,SVM)进行分类,得到交通标志的准确位置.实验表明该方法对亮度变化、视点变换、尺度变化及目标部分遮挡等情况具有很强的鲁棒性,并且查准率和查全率总体都比基于单特征的方法好.Traffic sign detection is important in intelligent transport system.In this paper,an efficient novel approach is proposed to achieve automatic traffic sign detection.The detection method combines color segmentation with learning based multi-features of traffic sign guided search.The rough location stage could obtain possible region of traffic sign using color segmentation based on YIQ space.The exact location stage searches traffic sign in these traffic sign possible regions based on multi-features fusion,we use histogram of oriented gradient(HOG) and local binary pattern(LBP) to classify by support vector machine(SVM).Experimental results show that,the proposed approach can achieve robustness to illumination,scale,viewpoint change and even partial occlusion.The average detection rate and the false positive rate of our approach are better than the method based on one feature.国防基础科研计划项目(B1420110155);国家重点基础研究发展计划(973)项目(2007CB311005);福建省教育厅A类项目(JA09230;JA09231