基于栈自编码器的图像分类器

图像分类问题包含两个重要的部分:特征提取器和分类器.多年来研究人员一直将精力投入到特征表示中,对于分类器却仅进行局部调参.基于一个性能优异的分类器与特征表示对图像分类系统同等重要的思想,提出了基于卷积特征的栈自编码器(stacked autoencoder on convolutional feature maps,SACF)的分类系统,并在数据集CUB-200和VGGflower上进行了实验,对比了SACF与基于卷积特征和多层感知机的卷积神经网络(CNN)分类系统的分类效果,实验结果表明SACF具有更优的分类效果.国家自然科学基金(61572409,61571188,61202143);;福建省自然科学基金(2013J05100);;中国乌龙茶产业福建省2011协同创新中心项目(闽教科[2015]75号);;福建省教育厅A类科技项目(JA13317

Hierarchical Image Automatic Annotation Based on Discriminative and Generative Models

图像自动标注是模式识别与计算机视觉等领域中重要而又具有挑战性的问题.针对现有模型存在数据利用率低与易受正负样本不平衡影响等问题,提出了基于判别模型与生成模型的新型层叠图像自动标注模型.该模型第一层利用判别模型对未标注图像进行主题标注,获得相应的相关图像集;第二层利用提出的面向关键词的方法建立图像与关键词之间的联系,并使用提出的迭代算法分别对语义关键词与相关图像进行扩展;最后利用生成模型与扩展的相关图像集对未标注图像进行详细标注.该模型综合了判别模型与生成模型的优点,通过利用较少的相关训练图像来获得更好的标注结果.在COrEl 5k图像库上进行的实验验证了该模型的有效性.Image automatic annotation is a significant and challenging problem in pattern recognition and computer vision.Aiming at the problems that the existing models have low utilization and they are affected by unbalanced positive and negative samples,a hierarchical image annotation model is proposed.In the first layer,discriminative model is used to assign topic annotations to unlabeled images,and then the corresponding relevant image sets are obtained.In the second layer,a keywords-oriented method is proposed to establish links between images and keywords,and then the proposed iterative algorithm is used to expand semantic words and relevant image sets.Finally,a generative model is used to assign detailed annotations to unlabeled images on expanded relevant image sets.Hierarchical model uses less relevant training images to obtain better annotation results.Experimental results on Corel 5K datasets verify the effectiveness of proposed hierarchical image annotation model.国家自然科学基金项目(No.60873179;60803078);高等学校博士学科点专项科研基金项目(No.20090121110032);深圳市科技计划基础研究项目(No.JC200903180630A)资

Graph Configuration System for Substation Integrated Automation Based on JGraph

JgrAPH是一套使用JAVA开发的、兼容SWIng的开源图形组件,具有丰富的图形操作接口和良好的可扩展性。该文基于JgrAPH实现一个跨平台的变电站综合自动化图形组态系统。与现有图形组态系统相比,该系统具有更好的自动处理能力,能实现电力系统设备的自动检测和接线图的自动生成,简化传统变电站图形系统在接线图设计方面的工作。JGraph is an open-source and Swing compatible graph component written in Java.JGraph provides a range of graph API and good extensibility.This paper implements a cross-platform graph configuration system of substation integrated automation based on JGraph, which is more automatic than the existing system.The feature of higher automation is proved by the function of automatic equipment0 recognition and automatic diagram design.It simplifies the diagram design of the tradition substation graph system.福建省重点科技基金资助项目(2006H0037

智能科学与技术专业本科培养方案在大类招生模式下的修订

大类招生模式是高校人才培养模式发展的新趋势。以厦门大学为例,介绍智能科学与技术专业本科培养方案在大类招生模式下的修订方案,探讨大类招生模式下智能科学与技术专业建设的几个问题

Extraction Model Based on Web Format Information Quantity in Blog Post and Comment Extraction

从信息论的角度出发,提出了一个基于网页格式信息量的博客文章和评论抽取模型.首先,结合网页视觉上的位置信息和文本的有效信息来定位网页正文.其次,利用博客网页中的格式信息作为信息单元并计算每个信息块所包含的格式信息量,通过计算最小切分位置信息量来切分正文中的文章和评论.该模型具有与语言无关的特点,因此具有一定的通用性.实验结果表明,该模型在博客正文定位和正文切分方面达到了较高的精确率.Based on the information theory,this paper presents a model based on Web format information quantity in blog information extraction.First,the vision information in blog Web page and the effective text information are combined to locate the main text which represents the theme of the blog Web page.Second,the format information of blog Web page is used to calculate the information quantity of each block and the minimal separating information quantity of separate position is used to detect the boundary of posts and comments in the main text.This model is language insensitive and can be used in a lot of blogs which are written in different natural languages.Experimental results show that this method achieves high precision in locating main text and separating the post and comment.国家重点基础研究发展计划(973)Nos.2004CB318109;2007CB311100;国家高技术研究发展计划(863)No.2007AA01Z441---

Action recognition based on the angle histogram of key parts

当前的姿态表示的行为识别方法通常对姿态的准确性做了很强的假设,而当姿态分析不精确时,这些现有方法的识别效果不佳。提出了一种低维的、鲁棒的基于关键肢体角度直方图的人体姿态特征描述子,用于将整个动作视频映射成一个特征向量。同时,还在特征向量中引入共生模型,用以表示肢体间的关联性。最后,设计了分层的SVM分类器,第1层主要用于选择高判别力的肢体作为关键肢体,第2层则利用关键肢体的角度直方图并作为特征向量,进行行为识别。实验结果表明,基于关键肢体角度直方图的动作特征具有较好的判别能力,能更好地区分相似动作,并最终取得了更好的识别效果。The current pose-based methods usually make a strong assumption for the accuracy of pose,but when the pose analysis is not precise,these methods cannot achieve satisfying results of recognition.Therefore,this paper proposed a low-dimensional and robust descriptor on the gesture feature of the human body based on the angle histogram of key limbs,which is used to map the entire action video into an feature vector.A co-occurrence model is introduced into the feature vector for expressing the relationship among limbs.Finally,a two-layer support vector machine( SVM) classifier is designed.The first layer is used to select highly discriminative limbs as key limbs and the second layer takes angle histogram of key limbs as the feature vector for action recognition.Experiment results demonstrated that the action feature based on angle histogram of key limbs has excellent judgment ability,may properly distinguish similar actions and achieve better recognition effect.国家自然科学基金资助项目(61202143); 福建省自然科学基金资助项目(2013J05100;2010J01345;2011J01367); 厦门市科技重点项目资助项目(3502Z20123017

A Novel Co-training Object Tracking Algorithm Based on Online Semi-supervised Boosting

基于自训练的判别式目标跟踪算法使用分类器的预测结果更新分类器自身,容易累积分类错误,从而导致漂移问题。为了克服自训练跟踪算法的不足,该文提出一种基于在线半监督bOOSTIng的协同训练目标跟踪算法(简称CO-SEMIbOOST),其采用一种新的在线协同训练框架,利用未标记样本协同训练两个特征视图中的分类器,同时结合先验模型和在线分类器迭代预测未标记样本的类标记和权重。该算法能够有效提高分类器的判别能力,鲁棒地处理遮挡、光照变化等问题,从而较好地适应目标外观的变化。在若干个视频序列的实验结果表明,该算法具有良好的跟踪性能。The self-training based discriminative tracking methods use the classification results to update the classifier itself.However,these methods easily suffer from the drifting issue because the classification errors are accumulated during tracking.To overcome the disadvantages of self-training based tracking methods,a novel co-training tracking algorithm,termed Co-SemiBoost,is proposed based on online semi-supervised boosting.The proposed algorithm employs a new online co-training framework,where unlabeled samples are used to collaboratively train the classifiers respectively built on two feature views.Moreover,the pseudo-labels and weights of unlabeled samples are iteratively predicted by combining the decisions of a prior model and an online classifier.The proposed algorithm can effectively improve the discriminative ability of the classifier,and is robust to occlusions,illumination changes,etc.Thus the algorithm can better adapt to object appearance changes.Experimental results on several challenging video sequences show that the proposed algorithm achieves promising tracking performance.国家自然科学基金(61201359;61202143); 福建省自然科学基金(2011J01367;2012J05126); 高等学校博士学科点专项科研基金(20090121110032)资助课

Hyperspectral image classification based on spectral-spatial combination features and graph cut

高光谱图像中存在着特征维度高而训练集小的问题。为解决该问题,提出了一种2步走的分类方法:1)通过支持向量机对图像进行初步分类,根据分类结果计算出每个类别的均值特征;2)使用1)计算出来的均值特征作为能量函数的数据项,然后利用图割原理对图像做二次分类。实验中发现:空间上相近的像素点往往具有相似的特征,且属于同一个类别。针对这种现象,提取一个将谱域特征和空域特征相结合的新特征。该特征既包含了光谱信息也包含了空间信息,具有较好的分类性能和鲁棒性。在IndIAn PInE数据集和PAVIA unIVErSITy数据集进行实验,实验结果表明了本文提出方法的有效性。The high-dimension of the feature vs.small-size of training set is an unsolved problem in the hyperspectral image classification task.To solve this problem a two-step classification method is proposed.Firstly,a preliminary classification is performed by the support vector machine( SVM) and the classification results are used to calculate the mean feature( MF) of each class.Secondly,a classification based on the graph cut theory is applied with the MFs as an input of the energy function.The experimental results showed that spatially nearby pixels have large possibilities of having the same label and similar features.Therefore,a new feature called spectral-spatial combination( SSC) is extracted that combines the spectral-based feature and spatial-based feature.The SSC feature contains the related spectral and spatial information of each pixel and provides better classification performance and robustness.Experiment results on the Indian Pine dataset and the Pavia University dataset demonstrated the effectiveness of the proposed method.国家自然科学基金资助项目(61202143); 福建省自然科学基金资助项目(2013J05100;2010J01345;2011J01367); 湖南省自然科学基金资助项目(12JJ2040

2017年深圳市学校乙型/Yamagata系流感传播动力学研究及防控措施评价

目的 探讨2017年深圳市乙型Yamagata系(B/Y)流感在学校暴发疫情中的传播能力,评价疫苗接种和隔离措施的防控效果。方法 运用SEIAR动力学模型对暴发现场调查数据进行模拟,计算疫情的基本再生数(R0...福田区卫生公益性科研项目(No.FTWS20160051

Study of Complex Long Sentence in English-Chinese Machine Translation System

目前的英汉机器翻译系统对简单句的分析已经到达了相当高的水平，但由于复杂长句的分析难度远高于简单句，对复杂长句的分析结果依然难以令人满意，其分析处理成为制约当前机器翻译系统的瓶颈。 本文采用传统的切分方法，借鉴语言学家对英语复杂长句的逻辑层次分析法，综合基于规则与基于统计的自然语言处理方法对复杂长句的分析处理进行了研究。本文的研究主要分成三个部分：切分规则、切分算法和拼合算法。 在切分规则上，本文从PennTreebank中获取规则。切分规则从基于单词的构建方式提升为基于语块的构建方式，提高了规则的抽象性，减少了规则的冗余。同时，将PennTreebank的基本标注集与chunk标注集进行映...Modern English-Chinese machine translation systems have already reached a quite high level in parsing a simple sentence, but they still have not given out gratifying results in parsing a complex long sentence for its complexity. In fact, effectively parsing a complex long sentence has become a strong restriction in machine translation systems. In order to solve the problem of parsing a complex ...学位：工学硕士院系专业：计算机与信息工程学院计算机科学系_计算机应用技术学号：20002801