搜索资源列表
bag_words_demo
- “袋子模型”特征描述演示,有注释,以及人脸库-bag of the words demo
bag_words_demo
- 利用词袋的方法进行物体识别,程序含有大量的实例-It is bag of words method to recognize objects, it includes a lot of examples
PG_BOW_DEMO-master
- 一个用BoW|Pyramid BoW+SVM进行图像分类的Matlab Demo -Image Classification using Bag of Words and Spatial Pyramid BoW
k-meansPBOF
- 一个用BOF程序,用k-means聚类,然后生成词袋。-A BOF program, K-means clustering, and then generate the bag of words.
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.
computeBoV
- Computing Bag of visual words
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
rough-set
- 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
hog
- 利用的图像处理中的梯度直方图,再加上词袋模型的知识,编得还可以-Gradient histogram of image processing, plus knowledge bag of words model series was also
lab-bow
- bag of words representation. Visual words.
similarityOfDocuments
- 利用词袋模型计算新闻的相关性,按照相似度由高到低返回新闻id-compute similarity of articals using bag of words model
project
- This program gives a program which follows the naive bayes classifier to classify the processed reviews.This used in sentiment classification.n document classification, a bag of words is a sparse vector of occurrence counts of words that is, a sparse
image_processing3
- 图像工程作业3:基于视词袋模型的场景识别 (Scene recognition with bag of words)-Image Engineering Job 3: Scene Recognition Based visual bag of words (Scene recognition with bag of words)
EyesClassifier
- It is a eye state classifier. You need to have 2 directories, one containing closed eyes and another one containig opened eyes. If you read and image, it will be classified as open or closed eyes using the method Bag of Visual Words
generateBOWFeatures
- Generate bag of words features
DBoW2-master
- 这是词袋模型2的测试程序,从外国的一个网站找到的,用于测试surf生成词袋文件-This is a bag of words model test procedure 2, a foreign website to find for testing surf generated word document bags
BagOfWordsDEMO
- BAG OF WORDS算法应用于图片分类。图像特征用sift算法描述,分类机利用了libsvm方法。-BAG OF WORDS algorithm is applied to image classification. Image features using sift algorithm descr iption, classification machine utilizes libsvm method.
BoV
- 一种场景分类的介绍,利用的是bag of visual words思想。-Introduction of a classification, using bag of visual words.
caffe-master
- 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 expe
homework3
- 将二位数据投影到一维线性, LDA(Latent Dirichlet Allocation)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。所谓生成模型,就是说,我们认为一篇文章的每个词都是通过“以一定概率选择了某个主题,并从这个主题中以一定概率选择某个词语”这样一个过程得到。文档到主题服从多项式分布,主题到词服从多项式分布。 [1] 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)