训练SVM是什么意思 [英] What it mean by Training SVM

问题描述

我是图像处理的新手.在我的项目中，我正在做使用SVM的图像分类器".我的最终软件是我选择一些图像并将其作为输入输入到软件中，它将对该图像进行分类.如果我给出动物的图像，它将适当地将其分类为猫或蛇".

I am new to image processing.As my project i am doing "image classifier using SVM".I have the idea of my final software "I select some image and give it as input to my software and it will classify that image .if i give the image of an animal it will classify it to cat or snake suitably"

当我在Google上搜索它时，它说首先您需要训练SVM"

When I google about it.it says "First you need to train SVM"

培训SVM是什么意思?

在我的情况下，对SVM的实际输入是什么(图像分类)?

SVM只是对图像进行分类的分类器.我是否有必要将图像秘密转换为任何特定格式?请帮助.

SVM is just a classifier how it classify images.Is it necessary for me to covert image to any particular format?.please help.

推荐答案

支持向量机(SVM)是一种用于监督数据分类的机器学习模型. SVM本质上是学习一个将数据空间分成2个区域(在2类情况下)的超平面.对于您的情况，假设您有蛇和猫的图像，并且需要对其进行分类.您需要遵循的步骤是

Support Vector Machine (SVM) is a machine learning model for supervised data classification. SVMs essentially learn a hyper-plane which separates the data space into 2 regions (in 2 class case). In your case, suppose you have images of snakes and cats and you need to classify them. The steps you'll need to follow are

从图像中提取功能".

Extract 'features' from the images.

在您的情况下，这些特征"可能是蛇/猫的外观的函数，例如动物的颜色，动物的形状等.通过将这些特征连接起来，可以获得多维特征向量.

These 'features' may be functions of appearance of snake/cat in your case e.g colour of the animal, shape of the animal etc. By concatenating these features you can get a multi-dimensional feature vector.

训练SVM分类器

Train an SVM classifier

训练本质上是学习 snake类和 cat类的特征向量之间的分离超平面.例如，如果您的特征向量是二维的，则训​​练SVM分类器将等于学习"一条最能区分您的标记数据/训练数据的线. 您可以使用众多免费的机器学习库中的任何一个.如果您使用python，可以使用 sklearn 来完成任务. 这种学习任务(线性SVM中的超平面)称为培训.

Training essentially learns a separating hyper-plane between the feature vectors of snake class and cat class . For example, if your feature vector is 2-dimensional, training an SVM classifier would amount to 'learning' a line which best separates your labeled-data/training-data. You could use any of the multitude of freely available libraries of machine learning. In case you speak python, you could use sklearn for the task. This task of learning (hyper-plane in linear SVM) is referred to training.

对图像进行分类.

Classify the images.

一旦您对模型进行了训练，就可以使用该模型对类别未知的图像进行分类.

Once you have trained your model, you could then use it classify images whose class is not known.

注意:我正在简化此答案涉及的许多细节/问题.我建议您阅读有关SVM的文章

Note: I am simplifying a lot of details/issues involved in this answer. I suggest you should read-up about SVM

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