Description: 自李菲菲提出bag of words 这个想法以来，借助于该思路的各种算法层出不穷，也表现非常不错的性能。该文件是在其人的tutorial上提供的一个Demo，有非常详细的注释，希望能给大家带来帮助-Since the LI Fei-made bag of words this idea since the idea of the various algorithms by means of an endless stream, but also doing a very good performance. This file is in its people' s tutorial available on a Demo, with very detailed notes, want to give us some help Platform: |
Size: 615424 |
Author:张俊格 |
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Description: 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的图像都用BoW模型描述了，就可以用SVM训练分类模型进行分类了。
在这里除了用SVM的RBF核，还自己定义了一种核： histogram intersection kernel，直方图正交核。因为很多论文说这个核好，并且实验结果很显然。能从理论上证明一下么？通过自定义核也可以了解怎么使用自定义核来用SVM进行分类。-Image features used in a Dense Sift, by the Bag of Words bag model to describe the word, of course, the training set is generally used to build the dictionary, because we do not test set. Although the test set is used as the test you, but who knows the practical application of the test image is valid, so I am here to build BoW dictionary only the training set.
In fact, BoW idea is very simple, although many people have asked me, but as long as you understand how to build a dictionary and how to image map to the dictionary D up on the line, and interviews are often asked me this question, do not know you all how to use vivid language to describe this problem?
After complete description of the image with BoW, refers to the training set and test set of images are described with the BoW model, the training of SVM classification model can be classified.
Apart from having to use the RBF kernel SVM, but also their own definition of a nuclear: histogram intersection kernel, histogram Platform: |
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Author:lipiji |
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Description: 大规模图像检索的代码，matlab与c++混合编程。总结了目前图像检索领域目前主要存在的方法。通过阅读该代码，可以对于经典的“词袋”模型（bow模型）有个具体的了解，但是该代码没有提供前序的特征提取，是直接从对提取好的特征向量聚类开始的，包括了k-means，分层k-means（HKM）聚类，倒排文件的建立和索引等，该代码还提供了局部敏感哈希（LSH）方法。最后，这份代码是下面这篇论文的作者提供的，
Indexing in Large Scale Image Collections: Scaling Properties and Benchmark-This C++/Matlab package implements several algorithms used for large scale
image search. The algorithms are implemented in C++, with an eye on large
scale databases. It can handle millions of images and hundreds of millions
of local features. It has MEX interfaces for Matlab, but can also be used
(with possible future modifications) from Python and directly from C++. It
can also be used for approximate nearest neighbor search, especially using
the Kd-Trees or LSH implementations.
The algorithms can be divided into two broad categories, depending on the
approach taken for image search:
1. Bag of Words:
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The images are represented by histograms of visual words.
It includes algorithms for computing dictionaries:
* K-Means.
* Approximate K-Means (AKM).
* Hierarchical K-Means (HKM).
It also includes algorithms for fast search:
* Inverted File Index.
* Inverted File Index with Extra Information (for example for implementing
Hamming Embedding).
* Platform: |
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Author:薛振华 |
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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. Platform: |
Size: 478208 |
Author:色楞格 |
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Description: 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 Platform: |
Size: 3573760 |
Author:赵宇 |
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Search - bag of words matlab