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Application of deep learning to computer vision

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机器学习基础：案例研究

华盛顿大学

4.6（8,566 个评分） | 200K 名学生已注册

课程 1（共 4 门，机器学习专项课程）

查看授课大纲

你是否好奇数据可以告诉你什么？你是否想在关于机器学习促进商业的核心方式上有深层次的理解？你是否想能同专家们讨论关于回归，分类，深度学习以及推荐系统的一切？在这门课上，你将会通过一系列实际案例学习来获取实践经历。在这门课结束的时候，

您将学习的技能

Python Programming, Machine Learning Concepts, Machine Learning, Deep Learning

审阅

4.6（8,566 个评分）

5 stars
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DS

Sep 28, 2015

Excellent course, with really good lectures, material and assignment. Plus the professors are really amazing and their enthusiasm is really refreshing and makes the class more interesting. Loved it!

GG

Jun 05, 2017

This course is very helpful for people who are novice in machine learning. The course uses Graphlab Create which is different from scikit or R-libraries, but the tool(Graphlab) is excellent to use.

从本节课中

Deep Learning: Searching for Images

You’ve probably heard that Deep Learning is making news across the world as one of the most promising techniques in machine learning. Every industry is dedicating resources to unlock the deep learning potential, including for tasks such as image tagging, object recognition, speech recognition, and text analysis.<p>In our final case study, searching for images, you will learn how layers of neural networks provide very descriptive (non-linear) features that provide impressive performance in image classification and retrieval tasks. You will then construct deep features, a transfer learning technique that allows you to use deep learning very easily, even when you have little data to train the model.</p>Using iPhython notebooks, you will build an image classifier and an intelligent image retrieval system with deep learning.

Application of deep learning to computer vision5:41

Deep learning performance3:05

Demo of deep learning model on ImageNet data2:57

Other examples of deep learning in computer vision1:30

Challenges of deep learning2:22

Deep Features6:44

教学方

Carlos Guestrin
Amazon Professor of Machine Learning
Emily Fox
Amazon Professor of Machine Learning

[MUSIC]

The first place where neural networks made a tremendous amount of difference,

is in an area called computer vision, so analyzing images and videos.

So let's see a few examples of how deep learning, or

this big neural networks, can be applied to computer vision.

So to do that, it's good to understand what image features are.

So in computer vision, image features are kind of like local detectors that get

combined to make a prediction.

So let's say we take this particular image.

Suppose that I want to predict whether this a face image or not a face image.

I run the neural detector, let's say a nose detector, eye detector,

another eye detector, a mouth detector, and

if all of these fire, you can do it and

using a little neural network, you can say this is a face, and that's our prediction.

Now, this is a simple example of how it can build a classifier for images, but

in reality they don't explicitly have a nose detector or eye detector.

What happens is these called image features or

interest points and there's various names for this.

But they really tried to find local image segments,

patches, that are really distinctive.

So then maybe they'll find the corner around the eye,

maybe the corner around the nose, so if you have lots of this corner detectors,

a face is comprised of corners.

Corner detector firings at places around the eye, the mouth, and both eyes.

And if enough of this fire in a particular pattern,

you discover that you have a face.

So this is how computer vision typically works.

So this is how classification works.

Of course, there's more general models and more complex ones, but

this is kind of the basic idea.

For years, these types of detectors of local features are built by hand.

A very popular one was called SIFT features.

And this retransformed their computer vision because they were really quite

applicable and quite cool.

And then, there are many others that improve the accuracy.

So other kinds of features that can be used.

We talked about this hand created image features like SIFT feature, and

so, let's talk about how they can be typically used for classification.

What we do is, we run the sifted textures over the image and

they fire in various places.

So for example the corners of the eyes and the mouth.

And then what we do is, we create a vector that describe the image based

on the firings, the locations where those SIFT features fired.

So, you might have some firings in some locations, no firings in other locations,

and this can be viewed similarly to the words in a document.

So, does the word messy appear?

Does the word football appear?

Similarly, does a corner appear in a particular place in the image.

Now once we have that description of the image, we feed it to a classifier.

So for example,

a simple linear classifier like we talked about earlier in the quarter.

It's not a quarter, we're teaching this online.

It was earlier in the module.

[LAUGH] So as we talked about earlier in

the module, you can feed it to a simple linear classifier and some names for those

are things like linguistically regression, support vector machines and more.

And from there, we get a detection as to whether this is image is a face or not.

Now that sounds pretty exciting and

it had a real significant impact in their computer vision.

The challenge though, is that creating these hand built image features was

a really complicated process and required several PhD thesis to be done well.

Neural networks are going to discover and learn those features automatically.

Let me give you an example of that.

Suppose they give you this input image and

they run it through a three layer neural network before making a prediction.

Typically what happens, is that you learn local feature detectors,

they're like SIFT, but at different levels and different layers.

And this detectors that you learn, they detect different things,

different properties of the image at different levels.

So, the first layer, you might learn detectors that look kinda

like these little patches, which really react to things like diagonal edges.

So this first detector here is all about capturing diagonal edges.

The center one is about capturing diagonal edges in the other direction.

And the last one here is about capturing transitions and color from dark to green.

Now, if we look at the next layer, you're combining this edge,

diagonal edge [INAUDIBLE] into some kind of more complex detectors.

So, for example, we discovered this wiggly line and

pattern detectors in the layer.

You also discovered this kind of detectors that react to corners,

that [INAUDIBLE] detect corners in the images.

And at the final layers you come up with detectors that are even more complicated.

So for a variety of images you might end up with

things that react to torsos and faces.

Or maybe if you have a bigger data set, even to these images of here,

which they fire up with images of corals.

So neural networks capture different types of image features at

different layers and then they get learned automatically.

[MUSIC]

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