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i am working on this project asssigned by university as final project. But the issue is i am not getting any help from the internet so i thought may be asking here can solve issue. i had read many articles but they had no code or guidance and i am confused what to do. Basically it is an image processing work with machine learning. Data set can be found easily but issue is python python learning algorithm and code
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I presume if it's your final project you have to create the program yourself rather than ripping it straight from the internet. If you want a good starting point which you can customise Tensor Flow from Google is very good. You'll want to understand how it works (i.e. how machine learning works) but as a first step there's a good example of image processing on the website in the form of number recognition (which is also the "Hello World" of machine learning).
https://www.tensorflow.org/get_started/mnist/beginners
This also provides a good intro to machine learning with neural nets: https://www.youtube.com/watch?v=uXt8qF2Zzfo
One note on Tensor Flow, you'll probably have to use Python 3.5+ as in my experience it can be difficult getting it on 2.7.
First of all I need to know what type of data are you using because depending on your data, if it is a MRI or PET scan or CT, there could be different suggestion for using machine learning in python for detection.
However, I suppose your main dataset consist of MR images, I am attaching an article which I found it a great overview of different methods>
This project compares four different machine learning algorithms: Decision Tree, Majority, Nearest Neighbors, and Best Z-Score (an algorithm of my own design that is a slight variant of the Na¨ıve Bayes algorithm)
https://users.soe.ucsc.edu/~karplus/abe/Science_Fair_2012_report.pdf
Here, breast cancer and colorectal cancer have been considered and the algorithms that performed best (Best Z-Score and Nearest Neighbors) used all features in classifying a sample. Decision Tree used only 13 features for classifying a sample and gave mediocre results. Majority did not look at any features and did worst. All algorithms except Decision Tree were fast to train and test. Decision Tree was slow, because it had to look at each feature in turn, calculating the information gain of every possible choice of cutpoint.
My Solution:-
Lung Image Database Consortium provides open access dataset for Lung Cancer Images.
Download it then apply any machine learning algorithm to classify images having tumor cells or not.
I attached a link for reference paper. They applied neural network to classify the images.
For coding part, use python "OpenCV" for image pre-processing and segmentation.
When it comes for classification part, use any machine learning libraries (tensorflow, keras, torch, scikit-learn... much more) as you are compatible to work with and perform classification using any better outperforming algorithms as you wish.
That's it..
Link for Reference Journal
Related
I have a mini project for my new course in Tensorflow for this semester with random topics. Since I have some background on Convolution Neuron Network, I intend to use it for my project. My computer can only run CPU version of TensorFlow.
However, as a new bee, I realize that there are a lot of topics such that MNIST, CIFAR-10, etc, thus I don't know which suitable topic I should pick out from them. I only have two weeks left. It would be great if the topic is not too complicated but too not easy for study because it matchs my intermediate level.
In your experience, could you give me some advice about the specific topic I should do for my project?
Moreover, it would be better if in this topic I can provide my own data to test my training, because my professor said that it is a plus point to get A grade in my project.
Thanks in advance,
I think that to answer this question you need to properly evaluate the marking criteria for your project. However, I can give you a brief overview of what you've just mentioned.
MNIST: MNIST is a Optical Character Recognition task for individual numbers 0-9 in images size 28px square. This is considered the "Hello World" of CNNs. It's pretty basic and might be too simplistic for your requirements. Hard to gauge without more information. Nonetheless, this will run pretty quickly with CPU Tensorflow and the online tutorial is pretty good.
CIFAR-10: CIFAR is a much bigger dataset of objects and vehicles. The image sizes are 32px square so individual image processing isn't too bad. But the dataset is very large and your CPU might struggle with it. It takes a long time to train. You could try training on a reduced dataset but I don't know how that would go. Again, depends on your course requirements.
Flowers-Poets: There is the Tensorflow for Poets re-training example which might not be suitable for your course, you could use the flowers dataset to build your own model.
Build-your-own-model: You could use tf.Layers to build your own network and experiment with it. tf.Layers is pretty easy to use. Alternatively you could look at the new Estimators API that will automate a lot of the training processes for you. There are a number of tutorials (of varying quality) on the Tensorflow website.
I hope that helps give you a run-down of what's out there. Other datasets to look at are PASCAL VOC and imageNet (however they are huge!). Models to look at experimenting with may include VGG-16 and AlexNet.
So I am creating my own classifiers using the OpenCV Machine Learning module for age estimation. I can train my classifiers but the training takes a long time so I would like to see some output (status classifier, iterations done etc.). Is this possible? I'm using ml::Boost, ml::LogisticalRegression and ml::RTrees all inheriting cv::StatModel. Just to be clear i'm not using the given application for recognizing objects in images (opencv_createsamples and opencv_traincascade). The documentation is very limited so it's very hard to find something in it.
Thanks
Looks like there's an open feature request for a "progress bar" to provide some rudimentary feedback... See https://github.com/Itseez/opencv/issues/4881. Personally, I gave up on using the OpenCV ML a while back. There are several high-quality tools available to build machine learning models. I've personally used Google's Tensorflow, but I've heard good things about Theano and Caffe as well.
Sorry this must be probably a dumb question. but i am fairly new to machine learning and Tessaract OCR. I have heard that Tessaract OCR can be trained.
What i need to know is does Tessaract OCR uses neural networks as their default training mechanism or do we have to program it explicitly to use neural networks ?.
Sorry if i'm thinking in a wrong way about this "training" concept. but what i need to know exactly is is Tessaract already using NN or if not how i can approach using NN with tessaract OCR to improve recognition accuracy ?.
If one can please suggest me some good resources/way to refer/try and to get started it would be a great help too.
what i currently know about basic machine learning supervised training concept and to perform basic image OCR operation in Tessaract OCR.
It appears that Tessaract uses an Adaptive Classifier by default. Check this out for a good read:
https://github.com/tesseract-ocr/docs/blob/master/tesseracticdar2007.pdf
There appears to be an option called "Cube mode" where it will switch to using NNs for the learning system instead of the adaptive classifier (https://code.google.com/p/tesseract-ocr-extradocs/wiki/Cube). More info about adaptive classifiers:
http://www.cs.indiana.edu/~rawlins/website/adaptivity/information-helper.html
Also, related very closely is a Learning Classifier System:
http://en.wikipedia.org/wiki/Learning_classifier_system
Also, your terminology of "training" is very close. Training is how you teach the pattern recognition system or learning system what responses it should give to certain input sets. Then, it uses similarities when it encounters unknown data to classify the new data. Machine learning is one of the coolest fields in existence in my opinion (probably biased opinion but whatever!) keep up the learning! You are the meta learner: learning how to teach a machine to learn! Cool stuff!
Yes, starting from tesseract 4.0, it provides a new lstm-based ocr engine: https://tesseract-ocr.github.io/tessdoc/NeuralNetsInTesseract4.00
I am trying to implement an efficient algorithm for nearest-neighbour search problem.
I have read tutorials about some data structures, which support operations for this kind of problems (for example, R-tree, cover tree, etc.), but all of them are difficult to implement.
Also I cannot find sample source code for these data structures. I know C++ and I am trying to solve this problem in this language.
Ideally, I need links that describe how to implement these data structures using source code.
There are several good choices of fast nearest neighbor search libraries.
ANN, which is based on the work of Mount and Arya. This work is documented in a paper by S. Arya and D. M. Mount. "Approximate nearest neighbor queries in fixed dimensions". In Proc. 4th ACM-SIAM Sympos. Discrete Algorithms, pages 271–280, 1993.
FLANN, which is based on the work of Marius Muja & Co. There is a paper by Marius Muja and David G. Lowe, "Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration", in International Conference on Computer Vision Theory and Applications (VISAPP'09), 2009. The code for FLANN is available on github
FLANN seems to be quicker in some cases, and is a more modern version of the code with solid bindings for a number of other languages, that can incorporate changes rapidly. ANN is probably a good choice if you want a solid well-tested standard library.
Edit in Response to Comment
Both of these libraries have extensive documentation and examples.
Sample code for ANN is available in the Manual, In section 2.1.4
Sample code for FLANN is available in the FLANN repository examples directory, for example /examples/flann_examples.c
You could try a linesweep algorithm to find the closest pair of points: http://community.topcoder.com/tc?module=Static&d1=tutorials&d2=lineSweep.
I have started learning Data Mining and wish to create a small project in C++/Java that allows me to utilize a database, say from twitter and then publish a particular set of results (for eg. all the news items on a feed). I want to know how to go about it? Where should I start?
This is a really broad question, so it's hard to answer. Here are some things to consider:
Where are you going to get the data? You mention twitter, but you'll still need to collect the data in some way. There are probably libraries out there for listening to twitter streams, or you could probably buy the data if someone is selling it.
Where are you going to store the data? Depending on how much you'll have and what you plan to do with it, a traditional relational database may or may not be the best fit. You may be better off with something that supports running mapreduce jobs out-of-the box.
Based on the answers to those questions, the choice of programming languages and libraries will be easier to make.
If you're really set on Java, then I think a Hadoop cluster is probably what you want to start out with. It supports writing mapreduce jobs in Java, and works as an effective platform for other systems such as HBase, a column-oriented datastore.
If your data are going to be fairly regular (that is, not much variation in structure from one record to the next), maybe Hive would be a better fit. With Hive, you can write SQL-like queries, given only data files as input. I've never used Mahout, but I understand that its machine learning capabilities are suited for data mining tasks.
These are just some ideas that come to mind. There are lots of options out there and choosing between them has as much to do with the particular problem you're trying to solve and your own personal tastes as anything else.
If you just want to start learning about Data Mining there are two books that I particularly really enjoy:
Pattern Recognition and Machine Learning. Christopher M. Bishop. Springer.
And this one, which is for free:
http://infolab.stanford.edu/~ullman/mmds.html
Good references for you are
AI course taught by people who actually know the subject,Weka website, Machine Learning datasets, Even more datasets, Framework for supporting the mining of larger datasets.
The first link is a good introduction on AI taught by Peter Norvig and Sebastian Thrun, Google's Research Director, and Stanley's creator (the autonomous car), respectively.
The second link you get you to Weka website. Download the software - which is pretty intuitive - and get the book. Make sure you understand all the concepts: what's data mining, what's machine learning, what are the most common tasks, and what are the rationales behind them. Play a lot with the examples - the software package bundles some datasets - until you understand what generated the results.
Next, go to real datasets and play with them. When tackling massive datasets, you may face several performance issues with Weka - which is more of a learning tool as far as my experience can tell. Thus I recommend you to take a look at the fifth link, which will get you to Apache Mahout website.
It's far from being a simple topic, however, it's quite interesting.
I can tell you how I did it.
1) I got the data using twitter4j.
2) I analyzed the data using JUNG.
You have to define a class representing edges and a class representing vertices.
These classes will contain the attributes of the edges and vertices.
3) Then, there is a simple function to add an edge g.addedge(V1,V2,edgeFromV1ToV2) or to add a vertex g.addVertex(V).
The class that defines edges or vertices is easy to create. As an example :
`public class MyEdge {
int Id;
}`
The same is done for vertices.
Today I would do it with R, but if you don't want to learn a new programming language, just import jung which is a java library.
Data mining is broad fields with many different techniques; classification, clustering, association and pattern mining, outlier detection, etc.
You should first decide what you want to do and then decide wich algorithm you need.
If you are new to data mining, I would recommend to read some books like Introduction to Data Mining by Tan, Steinbach and Kumar.
I would like to suggest you to use python or R for data mining process. Doing work with java or c , it bit difficult in the sense you need to do a lot coding