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Description: bag of feature 发展现状的介绍，北卡课程
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Size: 2846881 |
Author: zhangdd1@163.com |
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Description: SIFT两幅图像的算法.基于内容的图像检索系统 特征提取-Two images SIFT algorithms. Content-Based Image Retrieval System Feature Extraction
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Size: 4600832 |
Author: 王新胜 |
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Description: 这是图像识别方法bag of feature 的matlab源代码-This is image recognition method bag of feature matlab source code
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Size: 39936 |
Author: 峰峰 |
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Description: bag of feature bag of feature bag of feature-bag of feature bag of feature bag of feature bag of feature
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Size: 15360 |
Author: 黄泽铧 |
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Description: bag of feature bag of feature bag of feature bag of feature bag of feature -bag of feature bag of feature bag of feature bag of feature bag of feature bag of feature bag of feature bag of feature bag of feature bag of feature
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Size: 14336 |
Author: adai |
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Description: 基于Bag of word的图像分类经典文章，非常适合初学者学习-Bag of feature based classification
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Size: 763904 |
Author: leexingguo |
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Description: 基于Bag of feature的图像分类经典文章，非常适合初学者学习-Bag of feature based classification
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Size: 763904 |
Author: leexingguo |
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Description: 采用bag of word 特征加上颜色特征共同匹配的图像对匹配算法，具有匹配精度高的特点。-Bag of word feature by feature with the color image matching algorithm matches together, with matching high accuracy.
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Size: 24794112 |
Author: nedved |
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Description: matlab编写的bag of words，可以对目标进行特征提取，实现目标匹配识别。-Matlab prepared bag of words, the target feature extraction, to achieve the goal of matching recognition.
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Size: 478208 |
Author: 色楞格 |
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Description: Graphic Detection and Recognition using Bag-of-word method, finding feature vector with K-means classifier, training or testing data with Naive Bayes Classifier or PLCA method.
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Size: 32281600 |
Author: Huang Hua |
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Description: LLC做的图像分类算法，非常经典的图像分类算法。属于Bag-of-Feature模型，采用的是SIFT特征描述组。-LLC do image classification algorithm, belonging to the Bag-of-Feature model using the SIFT descriptor group
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Size: 986112 |
Author: quanmingyao |
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Description: Bag of Words is an implementation of feature extraction in Matlab
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Size: 20480 |
Author: johnaros |
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Description: 一个基于词汇包的训练的方法，内含作者论文。Bag of feature /training-A vocabulary packet-based training methods, including of paper. Bag of feature/training
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Size: 476160 |
Author: zhang |
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Description: 本程序针对光照变化和部分遮挡这两种情形,提出一种基于多帧视频图像的高稳定特征的交通标志识别方法。利用有交通标志的多帧视频图像的SURF特征建立bag of SURFs特征向量集,非常有利于对SURF研究的学者和研发人员进行学习和改造。-The program for the illumination changes and partial occlusion both cases, the paper proposes a high-stability characteristics of multi-frame video images of traffic sign recognition. There are traffic signs use SURF features multi-frame video images to establish bag of SURFs feature vector set, very beneficial for scholars and researchers to learn SURF research and transformation.
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Size: 1014784 |
Author: 刘恋 |
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Description: Bag of Feature and SVM example using opencv
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Size: 3072 |
Author: winev52 |
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Description: 将每一张图的特征点采样聚类成图片的视觉单词 即视觉单词,就是对应图片的代表 创建数据库,将每张图片的视觉单词入库,并建立索引-Will feature a map of each sampling point clustered into visual images of words that is visual words, is to represent the corresponding picture of the is created, the visual image of each word storage and indexing
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Size: 22851584 |
Author: 耿文浩 |
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Description: 用于创建BagOfFeature图片分类方法中的dictionary-Be used to create the dictionary for the Bag of Feature
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Size: 1024 |
Author: Rex |
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Description: Just like EM of Gaussian Mixture Model, this is the EM algorithm for fitting Bernoulli Mixture Model.
GMM is useful for clustering real value data. However, for binary data (such as bag of word feature) Bernoulli Mixture is more suitable.
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Size: 3072 |
Author: lin |
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Description: opencv bag of feature.BOW 分类方法，已经编译过。-opencv bag of feature
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Size: 5120000 |
Author: lin65505578 |
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Description: 种基于期望最大化( E M) 算法的局部图像特征的语义提取方法。首先提取图像的局部图像特 征， 统计特征在视觉词汇本中的出现频率， 将图像表示成词袋模型； 引入文本分析中的潜在语义分析技术建立从低层图像 特征到高层图像语义之间的映射模型； 然后利用 E M 算法拟合概率模型， 得到图像局部特征的潜在语义概率分布； 最后利 用该模型提取出的图像在潜在语义上的分布来进行图像分析和理解。-Semantic extraction of local image features based on expectation maximization (E M) algorithm. First extract the local features of the image, the visual vocabulary in the frequency of statistical feature, the image into the bag of words model introduce the latent semantic analysis of the text the technology to establish the mapping model between image low-level features to high-level semantic image and then use the E M algorithm for fitting probability model, probabilistic latent semantic distribution of local image features the distribution of the final image by using the model extracted in the latent semantic of image analysis and understanding.
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Size: 8817664 |
Author: 杨雪 |
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