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I'm new in computer vision area and I hope you can help me with some fundamental questions regarding CNN architectures.
I know some of the most well-known ones are:
VGG Net
ResNet
Dense Net
Inception Net
Xception Net
They usually need an input of images around 224x224x3 and I also saw 32x32x3.
Regarding my specific problem, my goal is to train biomedical images with size (80x80) for a 4-class classification - at the end I'll have a dense layer of 4. Also my dataset is quite small (1000images) and I wanted to use transfer learning.
Could you please help me with the following questions? It seems to me that there is no single correct answer to them, but I need to understand what should be the correct way of thinking about them. I will appreciate if you can give me some pointers as well.
Should I scale my images? How about the opposite and shrink to 32x32 inputs?
Should I change the input of the CNNs to 80x80? What parameters should I change mainly? Any specific ratio for the kernel and the parameters?
Also I have another problem, the input requires 3 channels (RGB) but I'm working with grayscale images. Will it change the results a lot?
Instead of scaling should I just fill the surroundings (between the 80x80 and 224x224) as background? Should the images be centered in this case?
Do you have any recommendations regarding what architecture to choose?
I've seen some adaptations of these architectures to 3D/volumes inputs instead of 2D/images. I have a similar problem to the one I described here but with 3D inputs. Is there any common reasoning when choosing a 3D CNN architecture instead of a 2D?
In advances I leave my thanks!
I am assuming you basic know-how in using CNN for classification
Answering question 1~3
You scale your image for several purposes. Smaller the image, the faster the training and inference time. However you will lose important information in the process of shrinking the image. There is no one right answer and it all depends on your application. Is real-time process important? If your answer is no, always stick to the original size.
You will also need to resize your image to fit the input size of predefined models if you plan to retrain them. However, since your image is in grayscale, you will need to find models trained in gray or create a 3 channel image and copy the same value to all R,G and B channel. This is not efficient but it will help you reuse the high quality model trained by others.
The best way i see for you to handle this problem is to train everything from start. 1000 can seem to be a small number of data, but since your domain is specific and only require 4 classes, training from scratch doesnt seem that bad.
Question 4
When the size is different, always scale. filling with the surrounding will cause the model to learn the empty spaces and that is not what we want.
Also make sure the input size and format during inference is the same as the input size and format during training.
Question 5
If processing time is not a problem RESNET. If processing time is important, then MobileNet.
Question 6
6) Depends on your input data. If you have 3D data then you can use it. More input data usually helps in better classification. But 2D will be enough to solve certain problem. If you can classify the images by looking at the 2D images, most probabily 2D images will be enough to complete the task.
I hope this will clear some of your problems and direct you to a proper solution.
I am studying neural networks, or more specifically image classification at the moment. While I was reading, I was wondering if the following has ever been done/ is doable. If anybody could point me to some sources or ideas, I'd appreciate it!
In a traditional neural network, you have a training data set of images and the weights of the neurons in the network. The goal is to optimize the weights so that the classification of the images is accurate for training data and new images is as good as possible.
I was wondering if you could reverse this:
Given a neural network and the weights to its neurons, generate a set of images corresponding to the classes that the network separates, i.e., a proto-type of the kinds of images this specific network is able to classify well.
In my mind it would work as follows (I'm sure this is not quite achievable, but just to get the idea across):
Imagine a neural network that is able to classify images containing labels cat, dog and neither of those.
What I want is the "inverse", i.e. an image of a cat, an image of a dog and one that is "furthest away" from the other two classes.
I think that this could be done by generating images and minimizing the loss function for one specific class, while maximizing it for all other classes at the same time.
Is this kind of how Google Deep Dream visualizes what it is "dreaming"?
I hope it is clear what I mean, if not I will answer any questions.
Is this kind of how Google Deep Dream visualizes what it is "dreaming"?
Pretty much, it seems, at least that's how the people behind it explain it:
One way to visualize what goes on [in a neural network layer] is to turn the network upside down and ask it to enhance an input image in such a way as to elicit a particular interpretation. Say you want to know what sort of image would result in “Banana.” Start with an image full of random noise, then gradually tweak the image towards what the neural net considers a banana (see related work [...]). By itself, that doesn’t work very well, but it does if we impose a prior constraint that the image should have similar statistics to natural images, such as neighboring pixels needing to be correlated.
Source - The whole blog post is worth reading.
I think you can understand the mainstream approach from Karpathy's blog:
http://karpathy.github.io/2015/03/30/breaking-convnets/
Normal ConvNet training: "What happens to the score of the correct class when I wiggle this parameter?"
Creating fooling images: "What happens to the score of (whatever class you want) when I wiggle this pixel?"
Fooling the classifier with an image is very close to what you ask. For your goal you need to add some regularization to your loss function to avoid fully misleading results - the absolute minimum loss can be very distorted picture.
I'm sure many people have already seen demos of using genetic algorithms to generate an image that matches a sample image. You start off with noise, and gradually it comes to resemble the target image more and more closely, until you have a more-or-less exact duplicate.
All of the examples I've seen, however, use a fairly straightforward pixel-by-pixel comparison, resulting in a fairly predictable 'fade in' of the final image. What I'm looking for is something more novel: A fitness measure that comes closer to what we see as 'similar' than the naive approach.
I don't have a specific result in mind - I'm just looking for something more 'interesting' than the default. Suggestions?
I assume you're talking about something like Roger Alsing's program.
I implemented a version of this, so I'm also interested in alternative fitness functions, though I'm coming at it from the perspective of improving performance rather than aesthetics. I expect there will always be some element of "fade-in" due to the nature of the evolutionary process (though tweaking the evolutionary operators may affect how this looks).
A pixel-by-pixel comparison can be expensive for anything but small images. For example, the 200x200 pixel image I use has 40,000 pixels. With three values per pixel (R, G and B), that's 120,000 values that have to be incorporated into the fitness calculation for a single image. In my implementation I scale the image down before doing the comparison so that there are fewer pixels. The trade-off is slightly reduced accuracy of the evolved image.
In investigating alternative fitness functions I came across some suggestions to use the YUV colour space instead of RGB since this is more closely aligned with human perception.
Another idea that I had was to compare only a randomly selected sample of pixels. I'm not sure how well this would work without trying it. Since the pixels compared would be different for each evaluation it would have the effect of maintaining diversity within the population.
Beyond that, you are in the realms of computer vision. I expect that these techniques, which rely on feature extraction, would be more expensive per image, but they may be faster overall if they result in fewer generations being required to achieve an acceptable result. You might want to investigate the PerceptualDiff library. Also, this page shows some Java code that can be used to compare images for similarity based on features rather than pixels.
A fitness measure that comes closer to what we see as 'similar' than the naive approach.
Implementing such a measure in software is definitely nontrivial. Google 'Human vision model', 'perceptual error metric' for some starting points. You can sidestep the issue - just present the candidate images to a human for selecting the best ones, although it might be a bit boring for the human.
I haven't seen such a demo (perhaps you could link one). But a couple proto-ideas from your desription that may trigger an interesting one:
Three different algorithms running in parallel, perhaps RGB or HSV.
Move, rotate, or otherwise change the target image slightly during the run.
Fitness based on contrast/value differences between pixels, but without knowing the actual colour.
...then "prime" a single pixel with the correct colour?
I would agree with other contributors that this is non-trivial. I'd also add that it would be very valuable commercially - for example, companies who wish to protect their visual IP would be extremely happy to be able to trawl the internet looking for similar images to their logos.
My naïve approach to this would be to train a pattern recognizer on a number of images, each generated from the target image with one or more transforms applied to it: e.g. rotated a few degrees either way; a translation a few pixels either way; different scales of the same image; various blurs and effects (convolution masks are good here). I would also add some randomness noise to the each of the images. The more samples the better.
The training can all be done off-line, so shouldn't cause a problem with runtime performance.
Once you've got a pattern recognizer trained, you can point it at the the GA population images, and get some scalar score out of the recognizers.
Personally, I like Radial Basis Networks. Quick to train. I'd start with far too many inputs, and whittle them down with principle component analysis (IIRC). The outputs could just be a similiarity measure and dissimilarity measure.
One last thing; whatever approach you go for - could you blog about it, publish the demo, whatever; let us know how you got on.
I am building a web application using .NET 3.5 (ASP.NET, SQL Server, C#, WCF, WF, etc) and I have run into a major design dilemma. This is a uni project btw, but it is 100% up to me what I develop.
I need to design a system whereby I can take an image and automatically crop a certain object within it, without user input. So for example, cut out the car in a picture of a road. I've given this a lot of thought, and I can't see any feasible method. I guess this thread is to discuss the issues and feasibility of achieving this goal. Eventually, I would get the dimensions of a car (or whatever it may be), and then pass this into a 3d modelling app (custom) as parameters, to render a 3d model. This last step is a lot more feasible. It's the cropping issue which is an issue. I have thought of all sorts of ideas, like getting the colour of the car and then the outline around that colour. So if the car (example) is yellow, when there is a yellow pixel in the image, trace around it. But this would fail if there are two yellow cars in a photo.
Ideally, I would like the system to be completely automated. But I guess I can't have everything my way. Also, my skills are in what I mentioned above (.NET 3.5, SQL Server, AJAX, web design) as opposed to C++ but I would be open to any solution just to see the feasibility.
I also found this patent: US Patent 7034848 - System and method for automatically cropping graphical images
Thanks
This is one of the problems that needed to be solved to finish the DARPA Grand Challenge. Google video has a great presentation by the project lead from the winning team, where he talks about how they went about their solution, and how some of the other teams approached it. The relevant portion starts around 19:30 of the video, but it's a great talk, and the whole thing is worth a watch. Hopefully it gives you a good starting point for solving your problem.
What you are talking about is an open research problem, or even several research problems. One way to tackle this, is by image segmentation. If you can safely assume that there is one object of interest in the image, you can try a figure-ground segmentation algorithm. There are many such algorithms, and none of them are perfect. They usually output a segmentation mask: a binary image where the figure is white and the background is black. You would then find the bounding box of the figure, and use it to crop. The thing to remember is that none of the existing segmentation algorithm will give you what you want 100% of the time.
Alternatively, if you know ahead of time what specific type of object you need to crop (car, person, motorcycle), then you can try an object detection algorithm. Once again, there are many, and none of them are perfect either. On the other hand, some of them may work better than segmentation if your object of interest is on very cluttered background.
To summarize, if you wish to pursue this, you would have to read a fair number of computer vision papers, and try a fair number of different algorithms. You will also increase your chances of success if you constrain your problem domain as much as possible: for example restrict yourself to a small number of object categories, assume there is only one object of interest in an image, or restrict yourself to a certain type of scenes (nature, sea, etc.). Also keep in mind, that even the accuracy of state-of-the-art approaches to solving this type of problems has a lot of room for improvement.
And by the way, the choice of language or platform for this project is by far the least difficult part.
A method often used for face detection in images is through the use of a Haar classifier cascade. A classifier cascade can be trained to detect any objects, not just faces, but the ability of the classifier is highly dependent on the quality of the training data.
This paper by Viola and Jones explains how it works and how it can be optimised.
Although it is C++ you might want to take a look at the image processing libraries provided by the OpenCV project which include code to both train and use Haar cascades. You will need a set of car and non-car images to train a system!
Some of the best attempts I've see of this is using a large database of images to help understand the image you have. These days you have flickr, which is not only a giant corpus of images, but it's also tagged with meta-information about what the image is.
Some projects that do this are documented here:
http://blogs.zdnet.com/emergingtech/?p=629
Start with analyzing the images yourself. That way you can formulate the criteria on which to match the car. And you get to define what you cannot match.
If all cars have the same background, for example, it need not be that complex. But your example states a car on a street. There may be parked cars. Should they be recognized?
If you have access to MatLab, you could test your pattern recognition filters with specialized software like PRTools.
Wwhen I was studying (a long time ago:) I used Khoros Cantata and found that an edge filter can simplify the image greatly.
But again, first define the conditions on the input. If you don't do that you will not succeed because pattern recognition is really hard (think about how long it took to crack captcha's)
I did say photo, so this could be a black car with a black background. I did think of specifying the colour of the object, and then when that colour is found, trace around it (high level explanation). But, with a black object in a black background (no constrast in other words), it would be a very difficult task.
Better still, I've come across several sites with 3d models of cars. I could always use this, stick it into a 3d model, and render it.
A 3D model would be easier to work with, a real world photo much harder. It does suck :(
If I'm reading this right... This is where AI shines.
I think the "simplest" solution would be to use a neural-network based image recognition algorithm. Unless you know that the car will look the exact same in each picture, then that's pretty much the only way.
If it IS the exact same, then you can just search for the pixel pattern, and get the bounding rectangle, and just set the image border to the inner boundary of the rectangle.
I think that you will never get good results without a real user telling the program what to do. Think of it this way: how should your program decide when there is more than 1 interesting object present (for example: 2 cars)? what if the object you want is actually the mountain in the background? what if nothing of interest is inside the picture, thus nothing to select as the object to crop out? etc, etc...
With that said, if you can make assumptions like: only 1 object will be present, then you can have a go with using image recognition algorithms.
Now that I think of it. I recently got a lecture about artificial intelligence in robots and in robotic research techniques. Their research went on about language interaction, evolution, and language recognition. But in order to do that they also needed some simple image recognition algorithms to process the perceived environment. One of the tricks they used was to make a 3D plot of the image where x and y where the normal x and y axis and the z axis was the brightness of that particular point, then they used the same technique for red-green values, and blue-yellow. And lo and behold they had something (relatively) easy they could use to pick out the objects from the perceived environment.
(I'm terribly sorry, but I can't find a link to the nice charts they had that showed how it all worked).
Anyway, the point is that they were not interested (that much) in image recognition so they created something that worked good enough and used something less advanced and thus less time consuming, so it is possible to create something simple for this complex task.
Also any good image editing program has some kind of magic wand that will select, with the right amount of tweaking, the object of interest you point it on, maybe it's worth your time to look into that as well.
So, it basically will mean that you:
have to make some assumptions, otherwise it will fail terribly
will probably best be served with techniques from AI, and more specifically image recognition
can take a look at paint.NET and their algorithm for their magic wand
try to use the fact that a good photo will have the object of interest somewhere in the middle of the image
.. but i'm not saying that this is the solution for your problem, maybe something simpler can be used.
Oh, and I will continue to look for those links, they hold some really valuable information about this topic, but I can't promise anything.
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I would like to compare a screenshot of one application (could be a Web page) with a previously taken screenshot to determine whether the application is displaying itself correctly. I don't want an exact match comparison, because the aspect could be slightly different (in the case of a Web app, depending on the browser, some element could be at a slightly different location). It should give a measure of how similar are the screenshots.
Is there a library / tool that already does that? How would you implement it?
This depends entirely on how smart you want the algorithm to be.
For instance, here are some issues:
cropped images vs. an uncropped image
images with a text added vs. another without
mirrored images
The easiest and simplest algorithm I've seen for this is just to do the following steps to each image:
scale to something small, like 64x64 or 32x32, disregard aspect ratio, use a combining scaling algorithm instead of nearest pixel
scale the color ranges so that the darkest is black and lightest is white
rotate and flip the image so that the lighest color is top left, and then top-right is next darker, bottom-left is next darker (as far as possible of course)
Edit A combining scaling algorithm is one that when scaling 10 pixels down to one will do it using a function that takes the color of all those 10 pixels and combines them into one. Can be done with algorithms like averaging, mean-value, or more complex ones like bicubic splines.
Then calculate the mean distance pixel-by-pixel between the two images.
To look up a possible match in a database, store the pixel colors as individual columns in the database, index a bunch of them (but not all, unless you use a very small image), and do a query that uses a range for each pixel value, ie. every image where the pixel in the small image is between -5 and +5 of the image you want to look up.
This is easy to implement, and fairly fast to run, but of course won't handle most advanced differences. For that you need much more advanced algorithms.
The 'classic' way of measuring this is to break the image up into some canonical number of sections (say a 10x10 grid) and then computing a histogram of RGB values inside of each cell and compare corresponding histograms. This type of algorithm is preferred because of both its simplicity and it's invariance to scaling and (small!) translation.
Use a normalised colour histogram. (Read the section on applications here), they are commonly used in image retrieval/matching systems and are a standard way of matching images that is very reliable, relatively fast and very easy to implement.
Essentially a colour histogram will capture the colour distribution of the image. This can then be compared with another image to see if the colour distributions match.
This type of matching is pretty resiliant to scaling (once the histogram is normalised), and rotation/shifting/movement etc.
Avoid pixel-by-pixel comparisons as if the image is rotated/shifted slightly it may lead to a large difference being reported.
Histograms would be straightforward to generate yourself (assuming you can get access to pixel values), but if you don't feel like it, the OpenCV library is a great resource for doing this kind of stuff. Here is a powerpoint presentation that shows you how to create a histogram using OpenCV.
Don't video encoding algorithms like MPEG compute the difference between each frame of a video so they can just encode the delta? You might look into how video encoding algorithms compute those frame differences.
Look at this open source image search application http://www.semanticmetadata.net/lire/. It describes several image similarity algorighms, three of which are from the MPEG-7 standard: ScalableColor, ColorLayout, EdgeHistogram and Auto Color Correlogram.
You could use a pure mathematical approach of O(n^2), but it will be useful only if you are certain that there's no offset or something like that. (Although that if you have a few objects with homogeneous coloring it will still work pretty well.)
Anyway, the idea is the compute the normalized dot-product of the two matrices.
C = sum(Pij*Qij)^2/(sum(Pij^2)*sum(Qij^2)).
This formula is actually the "cosine" of the angle between the matrices (wierd).
The bigger the similarity (lets say Pij=Qij), C will be 1, and if they're completely different, lets say for every i,j Qij = 1 (avoiding zero-division), Pij = 255, then for size nxn, the bigger n will be, the closer to zero we'll get. (By rough calculation: C=1/n^2).
You'll need pattern recognition for that. To determine small differences between two images, Hopfield nets work fairly well and are quite easy to implement. I don't know any available implementations, though.
A ruby solution can be found here
From the readme:
Phashion is a Ruby wrapper around the pHash library, "perceptual hash", which detects duplicate and near duplicate multimedia files
How to measure similarity between two images entirely depends on what you would like to measure, for example: contrast, brightness, modality, noise... and then choose the best suitable similarity measure there is for you. You can choose from MAD (mean absolute difference), MSD (mean squared difference) which are good for measuring brightness...there is also available CR (correlation coefficient) which is good in representing correlation between two images. You could also choose from histogram based similarity measures like SDH (standard deviation of difference image histogram) or multimodality similarity measures like MI (mutual information) or NMI (normalized mutual information).
Because this similarity measures cost much in time, it is advised to scale images down before applying these measures on them.
I wonder (and I'm really just throwing the idea out there to be shot down) if something could be derived by subtracting one image from the other, and then compressing the resulting image as a jpeg of gif, and taking the file size as a measure of similarity.
If you had two identical images, you'd get a white box, which would compress really well. The more the images differed, the more complex it would be to represent, and hence the less compressible.
Probably not an ideal test, and probably much slower than necessary, but it might work as a quick and dirty implementation.
You might look at the code for the open source tool findimagedupes, though it appears to have been written in perl, so I can't say how easy it will be to parse...
Reading the findimagedupes page that I liked, I see that there is a C++ implementation of the same algorithm. Presumably this will be easier to understand.
And it appears you can also use gqview.
Well, not to answer your question directly, but I have seen this happen. Microsoft recently launched a tool called PhotoSynth which does something very similar to determine overlapping areas in a large number of pictures (which could be of different aspect ratios).
I wonder if they have any available libraries or code snippets on their blog.
to expand on Vaibhav's note, hugin is an open-source 'autostitcher' which should have some insight on the problem.
There's software for content-based image retrieval, which does (partially) what you need. All references and explanations are linked from the project site and there's also a short text book (Kindle): LIRE
You can use Siamese Network to see if the two images are similar or dissimilar following this tutorial. This tutorial cluster the similar images whereas you can use L2 distance to measure the similarity of two images.
Beyond Compare has pixel-by-pixel comparison for images, e.g.,
If this is something you will be doing on an occasional basis and doesn't need automating, you can do it in an image editor that supports layers, such as Photoshop or Paint Shop Pro (probably GIMP or Paint.Net too, but I'm not sure about those). Open both screen shots, and put one as a layer on top of the other. Change the layer blending mode to Difference, and everything that's the same between the two will become black. You can move the top layer around to minimize any alignment differences.
Well a really base-level method to use could go through every pixel colour and compare it with the corresponding pixel colour on the second image - but that's a probably a very very slow solution.