I am newbie in deep learning, so not sure about the ability of deep learning. I am wondering if it is possible to use DL for pattern recognition. More specific, given many images containing different people wearing the same clothes or shoes. Can we tell it out that certain patterns in these images are the same, i.e. wearing the same clothes or shoes?
If yes, what is the pipeline to do it? From the beginning of data preparation to the end of classification/prediction? Any reference papers or blogs recommended? Thanks in advance!
Here are some examples I found online for better illustration:
enter link description here
enter link description here
From what I could gather from your problem statement, I believe if you require some general similarity you should go for an unsupervised clustering algorithm. It will create groups for your data set based on hidden similarities but you will have to label the clusters yourself which might not be very useful in your case.
Another approach could be to manually label your images with categories of interest for instance same background, same shoes, same clothes etc and train a multi class convolutional neural network.
Neural network would probably give you better results or you can even use the deep feather the neural net learned to extract features from your data and use a clustering algorithm on the extracted features.
This is an interesting time to be learning about deep learning. These tutorials might help you.
Let me know if I can help further.
Related
I am a very new student on machine learning. I just wanted to ask what are possible ways to improve a method (Naive Bayes for example) to get better results classifying images into text or non-text images, instead of just inputing a x number of images and telling the system which have text and which do not?
Thanks in advance
The state of the art in such problems are deep neural networks with several convolutional layers. See this article for an example of image classification using deep convolutional nets. Your problem (just determining if an image has text or not) is much easier than the general image classification problem the authors consider, so you'd probably get away with using a much simpler network architecture.
Nowadays you don't need to implement these things yourself, there are efficient and GPU-accelerated implementations freely available, for instance Caffe, Torch7, keras...
Dear all I am working on a project in which I have to categories research papers into their appropriate fields using titles of papers. For example if a phrase "computer network" occurs somewhere in then title then this paper should be tagged as related to the concept "computer network". I have 3 million titles of research papers. So I want to know how I should start. I have tried to use tf-idf but could not get actual results. Does someone know about a library to do this task easily? Kindly suggest one. I shall be thankful.
If you don't know categories in advance, than it's not classification, but instead clustering. Basically, you need to do following:
Select algorithm.
Select and extract features.
Apply algorithm to features.
Quite simple. You only need to choose combination of algorithm and features that fits your case best.
When talking about clustering, there are several popular choices. K-means is considered one of the best and has enormous number of implementations, even in libraries not specialized in ML. Another popular choice is Expectation-Maximization (EM) algorithm. Both of them, however, require initial guess about number of classes. If you can't predict number of classes even approximately, other algorithms - such as hierarchical clustering or DBSCAN - may work for you better (see discussion here).
As for features, words themselves normally work fine for clustering by topic. Just tokenize your text, normalize and vectorize words (see this if you don't know what it all means).
Some useful links:
Clustering text documents using k-means
NLTK clustering package
Statistical Machine Learning for Text Classification with scikit-learn and NLTK
Note: all links in this answer are about Python, since it has really powerful and convenient tools for this kind of tasks, but if you have another language of preference, you most probably will be able to find similar libraries for it too.
For Python, I would recommend NLTK (Natural Language Toolkit), as it has some great tools for converting your raw documents into features you can feed to a machine learning algorithm. For starting out, you can maybe try a simple word frequency model (bag of words) and later on move to more complex feature extraction methods (string kernels). You can start by using SVM's (Support Vector Machines) to classify the data using LibSVM (the best SVM package).
The fact, that you do not know the number of categories in advance, you could use a tool called OntoGen. The tool basically takes a set of texts, does some text mining, and tries to discover the clusters of documents. It is a semi-supervised tool, so you must guide the process a little, but it does wonders. The final product of the process is an ontology of topics.
I encourage you, to give it a try.
Can anyone advise me way to build effective face classifier that may be able to classify many different faces (~1000)?
And i have only 1-5 examples of each face
I know about opencv face classifier, but it works bad for my task (many classes, a few samples).
It works alright for one face classification with small number of samples. But i think that 1k separate classifier is not good idea
I read a few articles about face recognition but methods from these articles reqiues a lot of samples of each class for work
PS Sorry for my writing mistakes. English in not my native language.
Actually, for giving you a proper answer, I'd be happy to know some details of your task and your data. Face Recognition is a non-trivial problem and there is no general solution for all sorts of image acquisition.
First of all, you should define how many sources of variation (posing, emotions, illumination, occlusions or time-lapse) you have in your sample and testing sets. Then you should choose an appropriate algorithm and, very importantly, preprocessing steps according to the types.
If you don't have any significant variations, then it is a good idea to consider for a small training set one of the Discrete Orthogonal Moments as a feature extraction method. They have a very strong ability to extract features without redundancy. Some of them (Hahn, Racah moments) can also work in two modes - local and global feature extraction. The topic is relatively new, and there are still few articles about it. Although, they are thought to become a very powerful tool in Image Recognition. They can be computed in near real-time by using recurrence relationships. For more information, have a look here and here.
If the pose of the individuals significantly varies, you may try to perform firstly pose correction by Active Appearance Model.
If there are lots of occlusions (glasses, hats) then using one of the local feature extractors may help.
If there is a significant time lapse between train and probe images, the local features of the faces could change over the age, then it's a good option to try one of the algorithms which use graphs for face representation so as to keep the face topology.
I believe that non of the above are implemented in OpenCV, but for some of them you can find MATLAB implementation.
I'm not native speaker as well, so sorry for the grammar
Coming to your problem , it is very unique in its way. As you said there are only few images per class , the model which we train should either have an awesome architecture which can create better features within an image itself , or there should be an different approach which can achieve this task .
I have four things which I can share as of now :
Do data pre-processing and then create a bigger dataset and train on a neural network ideally. Here, we can do pre-processing like:
- image rotation
- image shearing
- image scaling
- image blurring
- image stretching
- image translation
and create atleast 200 images per class. Please checkout opencv documentation which provides many more methods on how you can increase the size of your dataset. Once you do this, then we can apply transfer learning , which is a better approach than training a neural network from scratch.
Transfer learning is a method where we train a network on our own custom classes , and this network is already pre-trained on 1000's of classes. Since our data here is very less, I would prefer transfer learning only. I have written a blog on how you can approach this using tranfer learning after you have the required amount of data. It is linked here. Face recognition also is a classification task itself, where each human is a separate class. So, follow the instructions given in the blog , may be it would help you create your own powerful classifer.
Another suggestion would be , after creating a dataset , encode them properly. This encoding would help you preserve the features in an image and can help you train better networks. VLAD ,Fisher , Bag of Words are few encoding techniques. You can search few repositories online which have implemented these already on ORL database. Once you encode , train the network on the encodings , you will obviously see a better performance.
Even do check out , Siamese network here which is meant for this purpose I feel . Here they compare two images with similar characteristics on different networks and there by achieve better classification accuracies . Git repository is here.
Another standard approach would be using SVM , Random forests since the data is less. If you still prefer neural networks the above methods would serve you the purpose. If you intend to go with encodings , then I would suggest random forests , as it is highly preferrable in learning and flexible too.
Hopefully , this answer would help you proceed in the right direction of achieving things.
You might want to take a look at OpenFace, a Python and Torch implementantion of face recognition with deep neural networks: https://cmusatyalab.github.io/openface/
Many machine learning competitions are held in Kaggle where a training set and a set of features and a test set is given whose output label is to be decided based by utilizing a training set.
It is pretty clear that here supervised learning algorithms like decision tree, SVM etc. are applicable. My question is, how should I start to approach such problems, I mean whether to start with decision tree or SVM or some other algorithm or is there is any other approach i.e. how will I decide?
So, I had never heard of Kaggle until reading your post--thank you so much, it looks awesome. Upon exploring their site, I found a portion that will guide you well. On the competitions page (click all competitions), you see Digit Recognizer and Facial Keypoints Detection, both of which are competitions, but are there for educational purposes, tutorials are provided (tutorial isn't available for the facial keypoints detection yet, as the competition is in its infancy. In addition to the general forums, competitions have forums also, which I imagine is very helpful.
If you're interesting in the mathematical foundations of machine learning, and are relatively new to it, may I suggest Bayesian Reasoning and Machine Learning. It's no cakewalk, but it's much friendlier than its counterparts, without a loss of rigor.
EDIT:
I found the tutorials page on Kaggle, which seems to be a summary of all of their tutorials. Additionally, scikit-learn, a python library, offers a ton of descriptions/explanations of machine learning algorithms.
This cheatsheet http://peekaboo-vision.blogspot.pt/2013/01/machine-learning-cheat-sheet-for-scikit.html is a good starting point. In my experience using several algorithms at the same time can often give better results, eg logistic regression and svm where the results of each one have a predefined weight. And test, test, test ;)
There is No Free Lunch in data mining. You won't know which methods work best until you try lots of them.
That being said, there is also a trade-off between understandability and accuracy in data mining. Decision Trees and KNN tend to be understandable, but less accurate than SVM or Random Forests. Kaggle looks for high accuracy over understandability.
It also depends on the number of attributes. Some learners can handle many attributes, like SVM, whereas others are slow with many attributes, like neural nets.
You can shrink the number of attributes by using PCA, which has helped in several Kaggle competitions.
This is a research question not a direct programming question.
I am working on a symbol recognition algorithm, What the software currently does, it takes an image, divide it into contours (blobs) and start matching each contour with a list of predefined templates. Then for each contour it takes the one that has the highest match rate.
The algorithm is doing fairely however I need to train it better. What I mean is this:
I want to use a machine learning algorithm that will train the algorithm to have better matching. So lets take an example:
I run the recognition on a symbol, the algorithm will run and find that this symbol is a car, then I have to confirm that result (maybe by clicking on "Yes" or "No") the algorithm should learn from that. So if I click on NO the algorithm should learn that this is not a car and will have better result next time (maybe try to match something else). while if i click on YES he will know that he was correct and next time he will perform better when searching for a car.
This is the concept I am trying to research. I need documents or algorithm that can achieve this sort of things. I am not looking for implementations or programming, just concept or researches.
I have done many researches and read a lot about machine learning, neural networks, decision trees.... but i was not able to know how can I use any in my scenarion.
I hope I was clear and this type of question is allowed on stack overflow. if not I'm sorry
Thanks a lot for any help or tip
Image recognition is still a challenge in the community. What you described in your process of manually clicking yes/no is just creating labeled data. Since this is a very broad area, I will just point you to a few links that might be useful.
To get start, you might want to use some existing image databases instead of creating your own, which saves you a lot of effort. e.g., this car dataset in UCIC image db.
Since you already have the background of machine learning, you can take a look at some survey paper that exactly match your project interests, e.g., search object recognition survey paper or feature extraction car in google.
Then you can dive into some good papers and see whether they are suitable for your project. For example, you can check the two papers below that linked with the UCIC image db.
Shivani Agarwal, Aatif Awan, and Dan Roth,
Learning to detect objects in images via a sparse, part-based representation.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(11):1475-1490, 2004.
Shivani Agarwal and Dan Roth,
Learning a sparse representation for object detection.
In Proceedings of the Seventh European Conference on Computer Vision, Part IV, pages 113-130, Copenhagen, Denmark, 2002.
Also check for some implemented softwares instead of starting from scratch, in your case, opencv should be good one to start with.
For image recognition, feature extraction is one of the most important step. You might want to check some stat-of-the-art algorithms in the community. (SIFT, mean-shift, harr features etc).
Boosting algorithm might also be useful when you reach the classification step. I see a lot of scholars mention this in image recognition community.
As #nickbar suggest, discuss more at https://stats.stackexchange.com/