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Description: 用经典K均值方法进行体育视频中的场景分类
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Author: whm |
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Description: 金字塔SIFT提取,用来做场景分类.
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Author: lswhwxyh |
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Description: 用经典K均值方法进行体育视频中的场景分类-K-means using the classic method of sports video scene classification
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Size: 19509248 |
Author: whm |
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Description: 基于边缘特征的图像配准算法源码
基于边缘特征的图像配准算法是将不同时间、不同的传感器（成像设备）或不同条件下（天候、照度、摄像位置和角度等）获取的同一场景的两幅或多幅图像根据其边缘特征进行匹配、叠加的处理，最终生成一幅全景图像的方法。该方法具有抗噪性强，匹配速度快，误匹配率低，适用于多种类型的图像等优点，主要可以运用于以下领域：
（1）军事研究领域，如飞行器辅助导航系绞、武器投射系统的末制导以及寻地等应用研究；
（2）医学图像分析，如数字剪影血管造影DSA血管造影术、肿瘤检测、白内障检测、CT、MRI、 PET、 SPECT图像结构信息融合；
（3）模式识别，如特征识别、波形分析、签名证实、目标物运动跟踪、序列图像分析等；
（4）计算机视觉，可用于目标定位、自动质量控制等；
（5）遥感数据分析，多电磁波段图像信息融合，如多谱段的等场景分类、定位和识别定义好的或已知特征的场景、自然资源监控、核生长监控、市区增长检测等。
本算法实现使用VC++6.0为开发环境，目前只支持灰度图像，感兴趣的朋友可以进一步扩展。-Image registration is a fundamental problem in image processing, which is a process to match two or more images of the same scene taken at different times, from different viewpoints, or by different sensors. Image registration is an important technique for a great variety of applications such as medical image analysis, computer vision, remote image data analysis, image information fusion, and the recovery of the 3–D characteristics of a scene.
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Size: 141312 |
Author: YYK |
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Description: 運動識別
在摄像机监视的场景范围内，对出现的运动目标进行检测、分类及轨迹追踪，可应用于各种监控目的，如周界警戒及入侵检测、绊线检测、非法停车车辆检测等。-Movement Recognition ' scene in the scope of surveillance cameras, the emergence of the moving target detection, classification and tracking, monitoring can be applied to a variety of purposes, such as perimeter security and intrusion detection, tripwire detection, detection of illegal parking of vehicles.
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Size: 40960 |
Author: zhangjc |
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Description: 模式识别中，多标签标记中的经典代码，主要用于场景分类，目标识别，结合svm和boost算法对自然场景进行分类，真的很不错，看看吧-Pattern Recognition, multi-tagged in the classic code, mainly used for scene classification, object recognition, combined with svm and boost the natural scene classification algorithm, it is really good, let' s see it
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Size: 1032192 |
Author: changfeng |
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Description: Biofeature 特征提取可用于场景分类-Biofeature feature extraction can be used for scene classification
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Size: 9185280 |
Author: 王强 |
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Description: 是一种纹理描述算子用于快速提取图像的纹理特征,应用于医学图像检索,场景分类等.-Is a texture description operator for rapid extraction of texture features, used in medical image retrieval, scene classification.
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Size: 3072 |
Author: zhangliyan |
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Description: 大名鼎鼎的方帅的博士学位论文---目前,计算机智能视频监控在理论和应用上都面临着很多难题,国内外大批学者投身于该领域的研究和探索,并且取得了大量的成果.本文是在这些成果的基础上,对计算机智能视频监控系统的关键技术进行研究.主要贡献可概括如下:首先,对目标检测技术进行了研究,并提出了一种基于背景建模的运动目标检测算法.利用统计的方法建立了基于颜色和颜色梯度的背景模型,并实时地对背景模型进行更新,最后将这两种背景模型综合考虑对目标进行了有效的检测.接着,研究了复杂背景下多目标跟踪问题,提出了基于蒙特卡罗粒子滤波器的复杂背景下多目标跟踪算法.然后,提出了目标分类算法.用有向无环图的多类支持向量机对目标进行分类,该分类器使用少量的样本,就可以得到较好的分类精度.最后,针对视频监控中的行为理解进行了探讨性的研究.建立一个行为理解的系统,这个系统由场景模型、行为模型和行为自动识别三部分组成.
-At present, the computer intelligence video frequency monitoring is facing many difficult problems in the theory and the application, the domestic and foreign large quantities of scholars join in this domain research and the exploration, and has made the massive progresses. This article is in these achievement foundation, conducts the research to the computer intelligence video frequency supervisory system s key technologies. The main contribution may summarize as follows: First, has conducted the research to the object detection technology, and proposed one kind based on the background modelling movement object detection algorithm. Has established using the statistical method based on the color and the color gradient background model, and real-time carries on the renewal to the background model, finally has carried on these two kind of background model overall evaluation to the goal the effective examination. Then, has studied under the complex background the multi-objective track que
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Size: 5263360 |
Author: 王一 |
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Description: 整理图像特征点提取和分类的程序（可以作为场景分类的前期工作），自己调试过能运行，特征点提取用的SIFT算法，使用K-means聚类算法，将得到的20个聚类中心写入txt文本中-Finishing the image feature point extraction and classification procedures (which can be as the preparatory work of the scene classification), their own debugging can run the SIFT feature point extraction algorithm, using K-means clustering algorithm, the 20 cluster centers will be writteninto the txt textcluster centers will be written to the txt text
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Size: 5055488 |
Author: 木易 |
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Description: 用于场景分类的代码，特征是事先提取好的，用PHOG特征-For scene classification code, characterized in that prior extraction good PHOG characterized
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Size: 2048 |
Author: 洪艳 |
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Description: 本文首先对原始图像提取了马尔可夫场特征、并引入了具有旋转不变性的LBP特征，随后利用目前机器学习领域和SVM一起构成最为重要的学习算法的AdaBoost方法进行特征的选取，合作，竞争和互补，以获得更好的场景分类的识别效果-In this paper, on the basis of the above framework, first extract the original image Markov field characteristics, and the introduction of a rotation invariant LBP features, followed by the use of the field of machine learning and SVM together constitute the most important learning algorithm AdaBoost method for featurethe selection of cooperation, competition and complement each other, to get a better scene classification recognition effect
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Size: 530432 |
Author: 季采风 |
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Description: 有关视频场景分类的几篇文章，都是比较经典的文章，适合入门者。-For articles classified video scenes are more classic article, suitable for beginners.
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Size: 12289024 |
Author: claire |
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Description: MIT的场景分类的技术报告,采用生物激励的方法-MIT' s scene classification of technical reports, using biologically inspired approach
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Size: 792576 |
Author: 于子予 |
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Description: 图像场景分类的bow模型opencv源代码，采用k-means聚类构造单词，采用支持向量机的svm分类器。-Image scene classification bow model opencv source code, using k-means clustering structure of words, using support vector machine svm classifier.
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Size: 10240 |
Author: 李翔 |
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Description: 图像场景分类中视觉词包分类的应用与操作代码-Review of the bag-of-visual-words models in image scene classification
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Size: 1112064 |
Author: libin |
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Description: ML-KNN，这是来自传统的K-近邻（KNN）算法。详细地，为每一个看不见的实例中，首先确定了训练集中的k近邻。之后，基于从标签集获得的统计信息。这些相邻的实例，即属于每个可能类的相邻实例的数量，最大后验（MAP）原理。用于确定不可见实例的标签集。三种不同现实世界中多标签学习问题的实验研究，即酵母基因功能分析、自然场景分类和网页自动分类，表明ML-KNN实现了卓越的性能(ML-KNN which is derived from the traditional K-nearest neighbor (KNN) algorithm. In detail, for each unseen
instance, its K nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of
these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, maximum a posteriori (MAP) principle
is utilized to determine the label set for the unseen instance. Experiments on three different real-world multi-label learning problems, i.e. Yeast
gene functional analysis, natural scene classification and automatic web page categorization, show that ML-KNN achieves superior performance
to some well-established multi-label learning algorithms.
2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.)
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Author: 玖
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Description: 基于分割的局部Sigma语义特征点，是对场景中的语义目标进行建模。先在传统的图像分割基础上，分割出场景的前景目标，再结合像素位置、颜色、Gabor特征和LBP特征[构造出表征目标语义信息的协方差描述子，最后将其转换成欧式空间下的Sigma点特征，适用于标准SVM的场景学习和分类。(The segmentation based local Sigma semantic feature points are modeling the semantic objects in the scene. In the traditional image segmentation based on the segmented foreground scene, we combine with the pixel location, color, Gabor and LBP feature [construct covariance descriptor represents the target semantic information, and finally converted into Euclidean space Sigma features, suitable for learning and classification standard to the scene SVM.)
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Author: delphi_zmy
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Description: 本文件是图像场景识别并进行分类的程序，已运行成功。
分别利用1 tiny image描述和最近邻分类器
2 bags of sifts描述和最近邻分类器
3bags of sifts描述和线性svm分类器进行场景分类识别的。
在主程序proj3中将FEATURE 改成tiny image，CLASSIFIER 改成nearest neighbor，注释其他FEATURE 和CLASSIFIER的选择就可以实现第一种场景分类识别：tiny image描述和最近邻分类器。以此类推就可以依次改FEATURE 和CLASSIFIER 形成第二种情况：bags of sifts描述和最近邻分类器和第三种情况：bags of sifts描述和线性svm分类器进行场景分类识别。(This document is an image scene recognition and classification of procedures have been run successfully.)
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Size: 18185216 |
Author: wujie123 |
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Description: 利用深度学习进行遥感图像场景分类
这里我们对NWPU-RESISC45数据集的场景图像进行分类
我们将卷积神经网络应用于图像分类。我们从头开始训练数据集。此外，还应用了预先训练的VGG16 abd ResNet50进行迁移学习。(Scene Classification of Remote Sensing Images Using Deep Learning
Here we classify scene images from NWPU-RESISC45 dataset
We apply convolutional neural network to image classification. We start training data sets from scratch. In addition, a pre-trained VGG16 abd ResNet50 is used for migration learning.)
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Author: 东哥12382 |
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