1.Introduction and backgrounds
作为本周的论文之一，这是一篇bag of features的基本文章之一，主要了解其中的基本思路，以及用到的基本技术，尽量使得细节更加清楚。
文章中比较了两个基本的方法，分别是：BAYES和SVM。
bag of keypoints的基本原理是：
A bag of keypoints corresponds to a histogram of the number
of occurrences of particular image patterns in a given image.
2. The main step
The main steps of our method are:
? Detection and description of image
patches 虽然patches 是小块的意思，但是这similar to the meaning of patten
? Assigning
patch descriptors to a set of predetermined clusters (a vocabulary)
第二部是对某一类的cluster 分配一个vocabulary
with a vector quantization algorithm
用一个矢量量化算法
? Constructing a bag of keypoints, which counts the number of
patches assigned to each
cluster实际转化为了计算hiastogram的分配问题，只是计算patten的histogram
? Applying a multi-class
classifier, treating the bag of keypoints as the feature vector, and thus
determine which category or categories to assign to the
image.采用一个多分类的分类器对histogram进行 classify，最后得到每种分类
3.Therefore the steps involved in training the system allow consideration of
multiple possible vocabularies:
? Detection and description of image patches
for a set of labeled training
images
? Constructing a set of vocabularies:
each is a set of cluster centres, with re-
spect to which descriptors are
vector quantized.
? Extracting bags of keypoints for these vocabularies
提取这些词汇的keypoints，这些keypoints的定义是如何呢？（We refer to the quantized feature vectors
(cluster centres) as “keypoints” by analogy with “keywords” in text
categorization.）所以实际上应该指的histogram
? Training multi-class classifiers using
the bags of keypoints as feature vectors 训练分类器，本文所采用两种分类器，分别所bayes分类器和SVM分类器
4.Feature extraction
就是特征的选取，作者一系列原因说明后，认为sift最好；
5. Visual vocabulary construction
总体目标：the vocabulary is a way of constructing a feature vector for
classification that relates “new” descriptors in query images to descriptors
previously seen in
training 实际就是建立相应的descriptor
经过一系列的说明，作者选择了比较常用的k-means算法作为vocabulaory building 的算法
但是k-means会带来两个问题：一、k-means仅仅对局部的最优化比较好；二、k-means的参数k是无法自己设定的，需要人工设定（作者解决方案是
多做几组，然后采用错误率最低的）
6.Categorization
(1) 贝叶斯分类
considering visual categorization, assume we have a set of labeled images I =
Ii and a vocabulary V = vi of representative keypoints (i.e. cluster centers).
Each
descriptor extracted from an image is labeled with the keypoint to which
it lies closest in feature space. We count the number N(t,i) of times keypoint
vi occurs in image Ii .
构造相应的分类特征，每个descriptor都被一个keypoint（空间内最近）所描述，然后计算每个keypoint在image I
中出现的概率，类似于计算histogram
P (C j | I i ) α P (C j )P (I i | C j
)
将条件概率的计算，转化为反方向的计算，也就是计算其在训练时得到的已知类别情况下的vocabulary出现概率，一旦出现新的输入image，那么就可以直接就算其每个类别的出现概率
(2) SVM 分类
SVM实际就是边界最大化的分类方法
In order to apply the SVM to multi-class problems we take the one-against-all
approach. Given an m-class problem, we train m SVM’s, each distinguishes images
from
some category i from images from all the other m-1 categories j not
equal to i. Given a query image, we assign it to the class with the largest SVM
output。
所采用的并不是所谓的多标签学习，还是一种二分的方法，先把一种类别同其他类别进行区分，然后再不断二分，如此继续。
7.实验结果
In the first we explore the impact of the number of clusters on classifier
accuracy and evaluate the performance of the Na?ve Bayes classifier. We then
explore the performance of the SVM on the same problem.
(1)贝叶斯方法 k=1000时效果比较好
(2)SVM中linear method gave the best performance (except in the case of cars
where a quadratic SVM gave better results)
SVM方法明显比贝叶斯方法好很多