Global image feature implementation

features

asked 2013-03-14 14:14:29 -0600

Shin
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Is there any OpenCV implementation of a global image features detector?

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answered 2013-03-14 14:47:08 -0600

StevenPuttemans

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Your question is kinda vague but, some of the possibilities are:

Using the color histogram or the grayscale histogram of an image and match that between images
You could use a histogram of oriented gradients by defining gradient directions and match that
You could define edges
...

So basically what you want to do is template matching. For that you first define a global descriptor of an image - http://docs.opencv.org/modules/imgproc/doc/histograms.html - http://docs.opencv.org/modules/imgproc/doc/feature_detection.html

Then you match the result of your references with the input images and use a distance metric between both. Best match has closest distance, meaning it is the most equal one.

Google template based matching, you will find tons of examples.

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@StevenPuttemans : Thank you for your answer. I'd like to use a global descriptor for a color image in combination with a global descriptor for a depth image (from which i can get a point cloud) and combine them in a single descriptor vector. My ultimate goal is to feed the descriptor vectors to a support vector machine. For this task, i want the descriptor to be scale and rotation invariant. Any suggestion?

Shin ( 2013-03-14 16:33:16 -0600 )edit

Good luck with that, there is no existance of a global scale and rotation invariant descriptor. That is why they created local descriptors, because the change in information locally is much smaller than globally. If you really want the global approach with the invariances, you could do a brute force approach, rotating your image over range of angles, then scaling your image over preset range, and apply color histogram based matching. But as I said, the reason why research went on with local feature matchers is this exact point!

StevenPuttemans ( 2013-03-15 03:30:35 -0600 )edit

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answered 2013-03-15 03:55:36 -0600

Guanta
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updated 2013-03-15 03:56:27 -0600

As Steven pointed out OpenCV doesn't have a global image descriptor. However you can use the local descriptors to form a local one. Please have a look at a previous answer by me at: http://answers.opencv.org/question/8677/image-comparison-with-a-database/#8686 , where you'll find more information how to achieve that. Good luck!

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Guanta, just in an addition to your answer, this is indeed the best way to go, but there are still global descriptors available. Creating a simple intensity histogram of a grayscale image is in fact a global descriptor. Doing this for the RGB images, will get you a color histogram, which is also a global descriptor. However, these global descriptors can be used as local features descriptors also.

StevenPuttemans ( 2013-03-15 04:08:48 -0600 )edit

Hehe, you are right :) ! I haven't thought about such 'simple' descriptors.

Guanta ( 2013-03-15 06:29:13 -0600 )edit

Probably since researchers gave up using these techniques like 20 years ago ... global features are only helpfull to further improve local feature detections in select regions.

StevenPuttemans ( 2013-03-15 06:42:40 -0600 )edit

@Guanta: Thank you for your answer. Actually the BoW approach using local features was my first thought but I haven't found examples on how to use BoW with multiple kind of features. I'm sorry to ask but could you provide minimal sample code?

Shin ( 2013-03-15 07:10:35 -0600 )edit

There exist already an example: samples/cpp/bagofwords_classification.cpp . Hope that helps you.

Guanta ( 2013-03-15 11:24:03 -0600 )edit

@Guanta: thank you. I have seen that example, but i am interested in mixing different kinds of features, combining descriptors from, say, sift AND surf, in single vectors and then getting bow descriptors. Is this approach feasible or i'm saying something wrong ?

Shin ( 2013-03-15 11:50:18 -0600 )edit

It perfectly possible.

Take a feature point.
Then apply SIFT descriptor.
Store it in a vector.
Apply SURF descriptor.
Store it behind the SIFT part in the vector.
Use the vector for BoW approach.

However, this seams overkill and will slow down the process drastically. Especially since you will need much more storage space for your description vectors.

StevenPuttemans ( 2013-03-15 14:35:14 -0600 )edit

There exist three other ways to use multiple features in BoW: You can omit the merging of the features but build multiple codebooks for both features and then either compute one descriptor by e.g. a weighted k-means or compute multiple independent histograms (=bow-descriptor) and merge them. Or third: Use the multiple histograms for independent classification and merge the classification results. For Stevens and my first approach the following paper may be interesting for you: http://icmll.buaa.edu.cn/members/jing.yu/YuanYuQinWan.pdf

Guanta ( 2013-03-16 08:09:36 -0600 )edit

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