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bofAndColor
- 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.
PG_BOW_DEMO
- 图像的特征用到了Dense Sift，通过Bag of Words词袋模型进行描述，当然一般来说是用训练集的来构建词典，因为我们还没有测试集呢。虽然测试集是你拿来测试的，但是实际应用中谁知道测试的图片是啥，所以构建BoW词典我这里也只用训练集。 其实BoW的思想很简单，虽然很多人也问过我，但是只要理解了如何构建词典以及如何将图像映射到词典维上去就行了，面试中也经常问到我这个问题，不知道你们都怎么用生动形象的语言来描述这个问题？ 用BoW描述完图像之后，指的是将训练集以及测试集
bag-0.1.1.tar
- bag of feature bag of feature bag of feature-bag of feature bag of feature bag of feature bag of feature
bag_words_demo
- bag of words 图像识别算法，斯坦福大学李飞飞实验室做的demo-bag of words
bag-of-words
- bag-of-words by R. Fergus, L. Fei-Fei and A. Torralba
BagOfWords
- 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
videopro
- matlab 视频处理，图像处理，视频播放，图像跟踪-matlab video processing, image processing, video playback, image tracking, etc., etc.
Bag-of-visual-words
- 基于bag of visual words 模型的人脸二分类代码，使用plsa等两种模式进行分类-Bag of visual words ,face recognition ,plsa
project
- Semantic similarities for bag of word
PG_BOW_DEMO
- bag of word,含特征提取，词包-bag of word proggramme
BagofWords
- 该论文在知网上付费下载，为2011年9月最新的关于Bag of Wo rds 算法的框架和基本内容，是学习bag of words算法的很好的入门参考。Bag of Words 算法是一种有效的基于语义特征提取与表达的物体识别算法, 算法充分学习文本检索算法的优点, 将图片整理为一系列视觉词汇的集合, 提取物体的语义特征, 实现感兴趣物体的有效检测与识别。-Bag of Word algo rithm is an efficient object r eco gnition alg or ith
bag_words_demo
- 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.
NlpReduce
- NLP-Reduce is a natural language query interface that allows its users to enter full English questions, sentence fragments, and keywords. It processes queries a s bag of words and only employs a reduced set of natural language processing techniques,
[0]-Bag-of-Word-Tutorial
- bow模型的详细讲解，全英文的，有兴趣的可以先看一下-the bow model explain in detail, all in English, interested can look at
computeBoV
- the Bag of Visual word ( BoV/BoW/BoF ) representation of an image or video
Bag-of-visual-words
- SIFT等局部特征的词袋模型实现。包括K-means聚类，直方图特征的形成，以及KNN分类。-SIFT local features such as word bag model implementation. Including K-means clustering to form histogram features, and KNN classification.
lda-c
- LDA是一种文档主题生成模型，也称为一个三层贝叶斯概率模型，包含词、主题和文档三层结构。文档到主题服从Dirichlet分布，主题到词服从多项式分布。 LDA是一种非监督机器学习技术，可以用来识别大规模文档集（document collection）或语料库（corpus）中潜藏的主题信息。它采用了词袋（bag of words）的方法，这种方法将每一篇文档视为一个词频向量，从而将文本信息转化为了易于建模的数字信息。但是词袋方法没有考虑词与词之间的顺序，这简化了问题的复杂性，同时也为
Synchronous-FIFO-
- 一个用verilog实现的同步fifo设计，压缩包里有word介绍设计中各信号的作用-Achieve a synchronous fifo with verilog design, compression bag has the role of word describes the design of the signals
Bag-of-visual-words
- 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 eac
mixBern
- 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.