I want to train a 3-class classifier with tissue images, but only have around 50 labelled images in total. I can't take patches from the images and train on them, so I am looking for another way to deal with this problem.
Can anyone suggest an approach to this? Thank you in advance.
The question is very broad but here are some recommendations:
It could make sense to generate variations of your input images. Things like modifying contrast, brightness or color, rotating the image, adding noise. But which of these operations, if any, make sense really depends on the type of classification problem.
Generally, the less data you have, the fewer parameters (weights etc.) your model should have. Otherwise it will result in overlearning, meaning that your classifier will classify the training data but nothing else.
You should check for overlearning. A simple method would be to split your training data into a training set and a control set. Once you have found that the classification is correct for the control set as well, you could do additional training including the control set.
Related
I have many evolution curves (on time), of a system as images.
These evolution curves are plotted when the system behave in a normal way ('ok').
I want to train a model, which learn the shapes of the curves (or parts of the shapes) when it behave in a normal way, so it will be able to classify new curves to normal (or abnormal).
Any ideas of the model to use, or how to proceed ?
Thank you
You can perform PCA, and then classify. Also look for functional data analysis
Here is a nice getting started guide with PCA
You can start with labeling (annotating) the images. The label can be as Normal/ Not Normal as 0/1 or as many classes you want to divide the data into.
Since it's a chart so the orientation is important, a wrong orientation can destroy the meaning of the image.
So make an algorithm which always orient the chart in the same way while reading.
Now that the labeling is done you need to train these images for correct classification.
Augment the data if needed
Find a image classification model
Use the trained weights
feed you images and annotations in the desired format
Train the model
Check for the output error or classification errors.
Create an evaluation matrix like confusion matrix in case of classification.
If the model is right and training is properly done you will get good accuracy.
Otherwise repeat the steps.
This is just an overview, with this you can start towards your goal.
Im currently working on a classification project but I'm in doubt about how I should start off.
Goal
Accurately classifying pictures of size 80*80 (so 6400 pixels) in the correct class (binary).
Setting
5260 training samples, 600 test samples
Question
As there are more pixels than samples, it seems logic to me to 'drop' most of the pixels and only look at the important ones before I even start working out a classification method (like SVM, KNN etc.).
Say the training data consists of X_train (predictors) and Y_train (outcomes). So far, I've tried looking at the SelectKBest() method from sklearn for feature extraction. But what would be the best way to use this method and to know how many k's I've actually got to select?
It could also be the case that I'm completely on the wrong track here, so correct me if I'm wrong or suggest an other approach to this if possible.
You are suggesting to reduce the dimension of your feature space. That is a method of regularization to reduce overfitting. You haven't mentioned overfitting is an issue so I would test that first. Here are some things I would try:
Use transfer learning. Take a pretrained network for image recognition tasks and fine tune it to your dataset. Search for transfer learning and you'll find many resources.
Train a convolutional neural network on your dataset. CNNs are the go-to method for machine learning on images. Check for overfitting.
If you want to reduce the dimensionality of your dataset, resize the image. Going from 80x80 => 40x40 will reduce the number of pixels by 4x, assuming your task doesn't depend on fine details of the image you should maintain classification performance.
There are other things you may want to consider but I would need to know more about your problem and its requirements.
To prepare large amounts of data sets for training deep learning-based image classification models, we usually have to rely on image augmentation methods. I would like to know what are the usual image augmentation algorithms, are there any considerations when choosing them?
The litterature on data augmentation is very very large and very dependent on your kind of applications.
The first things that come to my mind are the galaxy competition's rotations and Jasper Snoeke's data augmentation.
But really all papers have their own tricks to get good scores on special datasets for exemples stretching the image to a specific size before cropping it or whatever and this in a very specific order.
More practically to train models on the likes of CIFAR or IMAGENET use random crops and random contrast, luminosity perturbations additionally to the obvious flips and noise addition.
Look at the CIFAR-10 tutorial on TF website it is a good start. Plus TF now has random_crop_and_resize() which is quite useful.
EDIT: The papers I am referencing here and there.
It depends on the problem you have to address, but most of the time you can do:
Rotate the images
Flip the image (X or Y symmetry)
Add noise
All the previous at the same time.
I'm trying to set up an object classification system with OpenCV. When I detect a new object in a scene, I want to know if the new object belongs to a known object class (is it a box, a bottel, something unknown, etc.).
My steps so far:
Cutting down the Image to the roi where a new object could appear
Calculating keypoints for every Image (cv::SurfFeatureDetector)
Calculating descriptors for each keypoint (cv::SurfDescriptorExtractor)
Generating a vocabulary using Bag of Words (cv::BOWKMeansTrainer)
Calculating Response histograms (cv::BOWImgDescriptorExtractor)
Use the Response histograms to train a cv::SVM for every object class
Using the same set of images again to test the classification
I know that there is still something wrong with my code since the classification don't work yet.
But I don't really know, where I should use the full image (cutted down to the roi) or when I should extract the new object from the image and use just the object itself.
It's my first step into object recognition/classification and I saw people using both, full Images and extracted objects, but I just don't know when to use what.
I hope womeone can clarify this for me.
You should not use the same images for both testing and training.
In training, ideally you need to extract a ROI which includes just one dominant object, since the algorithm will assume that the codewords extracted from positive samples are the ones that should be presented in a test image to label it as positive. However, if you have a really big dataset like ImageNet, the algorithm should make a generalization.
In testing, you don't need to extract a ROI, because SIFT/SURF are scale invariant features. However, it's good to have a one dominant object in the test set, as well.
I think you should train 1 classifier for your each object class. This is called one-vs-all classifier.
One little note, if you don't want to worry about this issues and have big dataset. Just go with Convolutional Neural Networks. They have a really good generalization capability and are inherently multi-label thanks to their fully connected last layer.
I am trying to classify a series of images like this one, with each class of comprising images taken from similar cellular structure:
I've built a simple network in Keras to do this, structured as:
1000 - 10
The network unaltered achieves very high (>90%) accuracy on MNIST classification, but almost never higher than 5% on these types of images. Is this because they are too complex? My next approach would be to try stacked deep autoencoders.
Seriously - I don't expect any nonconvolutional model to work well on this type of data.
A nonconv net for MNIST works well because the data is well preprocessed (it is centered in the middle and resized to certain size). Your images are not.
You may notice (on your pictures) that certain motifs reoccure - like this darker dots - with different positions and sizes - if you don't use convolutional model you will not capture that efficiently (e.g. you will have to recognize a dark dot moved a little bit in the image as a completely different object).
Because of this I think that you should try convolutional MNIST model instead classic one or simply try to design your own.
First question, is if you run the training longer do you get better accuracy? You may not have trained long enough.
Also, what is the accuracy on training data and what is the accuracy on testing data? If they are both high, you can run longer or use a more complex model. If training accuracy is better than testing accuracy, you are essentially at the limits of your data. (i.e. brute force scaling of model size wont help, but clever improvements might, i.e. try convolutional nets)
Finally, complex and noisy data you may need a lot of data to make a reasonable classification. So you need many, many images.
Deep stacked autoencoders, as I understand it is an unsupervised method, which isn't directly suitable for classification.