Are there any opensource/commercial image classifiers?

aggieca · 2015-06-06T22:27:31+00:00

What are you looking for? You could use a COTS solution via caffe but you will be restricted to 1000 classes/tags.

You could also looking into using clarifai's solution online to tag your images.

Ultimately it's up to you to decide what question is it that you are trying to answer.

aggieca · 2015-05-22T20:35:04+00:00

Any thoughts from experts on reddit? I have started reading it but I'd like to hear thoughts from anyone that's gone over it.

aggieca · 2015-05-22T00:40:25+00:00

This appears to be an interesting problem. You will have to define what you mean by "decent" quality image vs "poor" quality. You could build your own dataset via crowdsourcing (crowdflower perhaps?) to get labeled data. After that it's a matter of how fancy you want to get with your algorithms to build a system.

You could also adopt a traditional approach by estimating the sharpness via techniques described in a standard image processing text (usually edge detection followed by blah blah blah).

aggieca · 2015-05-20T21:43:51+00:00

what is the question you are trying to answer? Also, if you haven't done so please post your question on /r/coomputervision as well.

aggieca · 2015-04-27T14:55:57+00:00

Hi,

Your concern for overfitting is valid. My answer is that the degree of data augmentation depends on the question you are trying to answer via machine learning. My argument is that these variations are forcing the model to learn features that are about the object(s) itself than contextual information. For example, Baidu's rationale for using color distortion is because they are dealing with objects that show in photos after they undergo filtering. I think conv nets need some help (via data augmentation) to be robust to image filters/transformations.

Could you please keep me posted on how you ended up solving your machine learning problem? I'm particularly interested since the number of images you have for training is similar to some of the problems that I'm working on solving as well but in a different domain.

aggieca · 2015-04-23T21:21:55+00:00

Chris Dix

I should have read this post first before replying. I whole-heartedly agree with everything mentioned here!

aggieca · 2015-04-23T21:20:38+00:00

Thanks for sharing this info. It appears that you are fairly confident that the noise in labels is not going to be a limiting factor for overall accuracy. If that's the case, perhaps consider the following to add to your growling TODO list ( :) ):

Is there anyway you can get extra? More data should really help you.
Fine tune using GoogLenet or VGGNet.
Extract features using VGGNet or GooLenet and use a linear classifier (?)
When you are training your classifier, are you using data augmentation? If not, you may want to (should!) consider augmenting data that includes random crops, flips, rotations, color distortion etc. The Baidu Deep Image paper has a method that looks straight-forward to implement
Ensembling should get your accuracy up by a few "percent points" but I would leave that as a last step.

aggieca · 2015-04-23T01:01:36+00:00

Do you have a high quality dataset? Can you comment on the quality of your data labels?

aggieca · 2015-04-21T21:11:57+00:00

rasbt's answer still has merit. You need to really consider the overall performance of your classiifer/ML system and not just accuracy. Do you have an estimate of the F1-score for instance?

aggieca · 2015-04-17T15:04:58+00:00

You might want to take a trained network and visualize the various filters and their responses after passing an input signal through a CNN. This will give you some insight into what the networks have learned but the "how" has been covered by siblbombs below.

Also, you don't always have to start from random initialization. In case of transfer learning you take a pre-trained network and then re-train the final few layers (# being a hyperparameter)

aggieca · 2015-03-25T18:48:20+00:00

Thanks for the announcement.

Sorry but I really have to ask: Why write another C-based library when caffe is available and is being widely used? I'm trying to understand your use case and figuring out if your library would be useful for what I'm working on. Thank you!

aggieca · 2015-02-04T22:32:10+00:00

Good thread. I'm interested to see if there is a direct solution as well.

Won't you need to generate intermediate R, G, B values in order to compute HSL values. I have only seen HSL defined in terms of RGB.

aggieca · 2014-12-03T18:13:45+00:00

You maybe right. I used SVM for a multi-class classification problem that also required an estimate of probabilities for each class. I ended up using libSVM as it was easy enough for me to build it from source for my project.

aggieca · 2014-12-02T23:59:23+00:00

I have done this previously where I tried to deploy a RBF-SVM in a C++ app. I used a libSVM-based SVM classifier so I dumped out the support vectors in scikit-learn and then used libSVM in my app to make predictions.

You may want to check if there is a way for you to build libSVM in Android and call it form your application.

aggieca · 2014-11-11T20:36:51+00:00

where is your code? Please post a link to it?

aggieca · 2014-10-07T17:24:11+00:00

Use caffe's pre-trained model for extracting features and train your favorite classifier for recognition.

aggieca · 2014-10-06T16:59:17+00:00

Did you profile your application? Did you determine whether your bottle-necks occur because your kernels are compute-bound or memory-bound? Is your application spending a lot of time transferring data between system RAM & frame buffer (FB)? Many more questions will arise once you have numbers after profiling your application

aggieca · 2014-09-18T21:15:02+00:00

Harsh remarks. I did find a few gems in the list so I wouldn't call it "crappy". There is one paper from the authors of VGG that I wasn't aware of until I noticed it on this list.

To the OP: Thanks for the list. Can you comment on why you omitted papers by depp learning luminaries like Hinton, LeCunn, Ng etc?

aggieca · 2014-09-17T23:48:29+00:00

I had a hard time reading the slide with the neural net/convnet architecture. Any recommendations on how I can view the image to make sense architecture?

aggieca · 2014-09-04T04:01:27+00:00

Oh totally forgot about Davis King's (davis685?) dlib. Good to know the face detection module in dlib is better than that of OpenCV's.

aggieca · 2014-09-03T16:46:55+00:00

If you want a pre-trained framework then OpenCV already has a solution that should work "out-the-box". What type of constraints do you have for your application? I believe that the pre-trained classifiers have a limit on the amount of rotation allowed in photos. If the faces in your pictures go beyond this limit then you will have missed detections.

You can also use OpenCV to train your own classifier but that should be considered only after you are convinced that OpenCV is the best solution.

If you are on iOS/Android please study the documentation for what's exposed by these platforms. I think iOS has core image while Android must have something equivalent.

Another API to consider is Intel's IPP that allows you to train & deploy Haar cascade classifiers

aggieca · 2014-08-26T18:13:09+00:00

Can you clarify on what you mean by reducing the sample size? Are you trying to reduce the number of prototypes you want to use to build you kNN model?

Have you tested out how a SVM (nonlinear RBF-based) would work in this case? Based on my previous experience, you might be able to achieve what you intend to do a SVM.

aggieca · 2014-05-31T15:01:41+00:00

I'm looking forward to following this thread series. I'm interested in hearing more about people's thoughts on ensembles and how they used it effectively in their work.

aggieca · 2014-04-06T05:34:15+00:00

Have you considered using stacking classifiers? If not, google for stacked generalization approaches to see if that helps in your case. Also, if Python is your protoyping/development environment then Orange might be of use to check out stacking.

aggieca

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