CODEWITHSUNDEEP
image-processingcomputer-visionimage-recognition
Dan Mazzini
Dan Mazzini

Reputation: 1001

Image classification/recognition open source library

I have a set of reference images (200) and a set of photos of those images (tens of thousands). I have to classify each photo in a semi-automated way. Which algorithm and open source library would you advise me to use for this task? The best thing for me would be to have a similarity measure between the photo and the reference images, so that I would show to a human operator the images ordered from the most similar to the least one, to make her work easier.

To give a little more context, the reference images are branded packages, and the photos are of the same packages, but with all kinds of noises: reflections from the flash, low light, imperfect perspective, etc. The photos are already (manually) segmented: only the package is visible.

Back in my days with image recognition (like 15 years ago) I would have probably tried to train a neural network with the reference images, but I wonder if now there are better ways to do this.

Upvotes: 4

Views: 4216

Answers (4)

Rabindra Nath Nandi
Rabindra Nath Nandi

Reputation: 1461

Now-a-days Deep Learning based framworks like Torch , Tensorflow, Theano, Keras are the best open source tool/library for object classification/recognition tasks.

Upvotes: 0

Radim Burget
Radim Burget

Reputation: 1516

Try Radpiminer (one of the most widely used data-mining platform, http://rapid-i.com) with IMMI (Image Mining Extension, http://www.burgsys.com/mumi-image-mining-community.php), AGPL licence.

It currently implements several similarity measurement methods (not only trivial pixel by pixel comparison). The similarity measures can be input for a learning algorithm (e.g. neural network, KNN, SVM, ...) and it can be trained in order to give better performance. Some information bout the methods is given in this paper: http://splab.cz/wp-content/uploads/2012/07/artery_detection.pdf

Upvotes: 0

ely
ely

Reputation: 77434

I recommend that you use Python, and use the NumPy/SciPy libraries for your numerical work. Some helpful libraries for handling images are the Mahotas library and the scikits.image library.

In addition, you will want to use scikits.learn, which is a Python wrapper for Libsvm, a very standard SVM implementation.

The hard part is choosing your descriptor. The descriptor will be the feature you compute from each image, intended to compute a similarity distance with the set of reference images. A good set of things to try would be Histogram of Oriented Gradients, SIFT features, and color histograms, and play around with various ways of binning the different parts of the image and concatenating such descriptors together.

Next, set aside some of your data for training. For these data, you have to manually label them according to the true reference image they belong to. You can feed these labels into built-in functions in scikits.learn and it can train a multiclass SVM to recognize your images.

After that, you may want to look at MPI4Py, an implementation of MPI in Python, to take advantage of multiprocessors when doing the large descriptor computation and classification of the tens of thousands of remaining images.

The task you describe is very difficult and solving it with high accuracy could easily lead to a research-level publication in the field of computer vision. I hope I've given you some starting points: searching any of the above concepts on Google will hit on useful research papers and more details about how to use the various libraries.

Upvotes: 5

dranxo
dranxo

Reputation: 3388

The best thing for me would be to have a similarity measure between the photo and the reference images, so that I would show to a human operator the images ordered from the most similar to the least one, to make her work easier.

One way people do this is with the so-called "Earth mover's distance". Briefly, one imagines each pixel in an image as a stack of rocks with height corresponding to the pixel value and defines the distance between two images as the minimal amount of work needed to transfer one arrangement of rocks into the other.

Algorithms for this are a current research topic. Here's some matlab for one: http://www.cs.huji.ac.il/~ofirpele/FastEMD/code/ . Looks like they have a java version as well. Here's a link to the original paper and C code: http://ai.stanford.edu/~rubner/emd/default.htm

Upvotes: 1

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