I am working with SVM-light. I would like to use SVM-light to train a classifier for object detection. I figured out the syntax to start a training:
svm_learn example2/train_induction.dat example2/model
My problem: how can I build the "train_induction.dat" from a
set of positive and negative pictures?
There are two parts to this question:
What feature representation should I use for object detection in images with SVMs?
How do I create an SVM-light data file with (whatever feature representation)?
For an intro to the first question, see Wikipedia's outline. Bag of words models based on SIFT or sometimes SURF or HOG features are fairly standard.
For the second, it depends a lot on what language / libraries you want to use. The features can be extracted from the images using something like OpenCV, vlfeat, or many others. You can then convert those features to the SVM-light format as described on the SVM-light homepage (no anchors on that page; search for "The input file").
If you update with what language and library you want to use, we can give more specific advice.
Related
I want to find images similar to another image. So after researching i found two methods first was two represent the image by its attributes like
length = full
pattern = check
color = blue
but the limitation of this method is that I will not be able to get an exhaustive dataset with all the features marked.
The second approach I found was to extract features and do feature mapping.
So I decided to use deep convolution neural networks with caffe so that by using any of the exsisting models I could learn the features and then perform feature matching or some other operation. I just wanted to take a general advice what can be the other methods which are good and worth a try. And since I am just starting out with caffe so can anyone give a general guideline how to approach the problem with caffe?
Thanks in advance
I looked at phash just was curious that it will find the images which are same like there are minor intensity variations and some other variation wiill it also work to give the same type(semantically) like for a tshirt with blue and red stripes will it give black and white stripe as similar and would it consider things like the length of shirt, collar style etc
It's true, that it's been empirically shown, that the euclidean distance between the features extracted using ConvNets is closer for images of the same class, while farther for images of different classes - but it's important to understand what kind of similarity you're looking for.
One can define many types of similarity measures, and the type of features you use (in the case of ConvNets, the type of data it was trained on) affects the kind of similar images you'll get. For instance, maybe given an image of a dog, you want to find other pictures of dogs but not specifically that exact dog, alternatively, maybe you have a picture of a church and you want to find another image of the exact same church but from a different angle - these are two very different problems, with different methods you can use to solve them.
One particular kind of convolutional neural networks you can look at, are Siamese Network, which are built to learn similarities between two images, given a dataset of pairs of images with the labels same/not_same. You can look for implementation in Caffe for this method here.
A different method, is to take a ConvNet trained on ImageNet data (see here for options), and use the python/matlab interface to classify images, and then extract the second to last layer, and use that as the representation for that image. Now you can just take the euclidean distance of those representations and this would be your similarity measure.
Unrelated to Caffe, you can also use "old school" methods of feature matching, included in open source libraries like OpenCV (an example tutorial of such method).
I am using bag of visual words to cluster the features of the image. So far all the work i came across used BOW for clustering features computed using SIFT, SURF, etc. Maybe I missed this out but is it possible to represent Color Histogram Feature into BOW and also Edge oriented Histogram feature in BOW.
I am working on image classifier and I want to used SIFT with RGB color Histogram as feature descriptors in Opencv. So i was wondering is it correct to concatenate the 2 feature vectors into one and add to BOW or is it correct to add SIFT feature to BOW and concatenate the Histogram feature to BOW (I am using this model as of now but I am wondering which one is correct.)
Historically we use bag-of-words model for document retrieval.
As mentioned in this post, in computer vision bag-of-visual-words refers to bag-of-features (BOF). If you want to use two types of features, you can concatenate the two feature vectors and build the BOF model. (As an example,) For human action recognition task people often models human action using BOF built on the concatenation of appearance feature (Gradient -- HOG) and motion feature (Optical Flow -- HOF).
This may sound very naive but i just wanted to be sure that when talking in Machine Learning terminology, features in Document Clustering is words which are chosen from a document, if some are discarded after stemming or as stop-words.
I am trying to use LibSvm library and it says that there are different approaches for different types of { no_of_instances, no_of_features }.
Like if no_of_instances is much lower than no_of_features, linear kernels would do. if both are large, linear would be fast. However, if no_of_features is small, non-linear kernels is better.
So for my document clustering/classification, i have small number of documents like 100 each may have words around 2000. So i fall in small no_of_instances and large no_of_features category depending upon what i consider a feature is.
I would like to use tf-idf for the document.
So Is no_of_features is the size of the vector that i get from tf-idf ?
What you are talking about here is just one of possibilities, in fact the most trivial way of defining features for documents. In machine learning terminology feature is any mapping from the input space (in this particular example - from space of documents) into some abstract space, which is suited for a particular machine learning model. Most of the ML models (like neural networks, support vector machines, etc.) work on the numerical vectors, so features has to be mappings from documents to (constant size) vectors of numbers. This is a reason for sometimes choosing a representation of bag of owrds, where we have a words' count vector as a document representation. This limitation can be overcomed by using specific models, like for example Naive Bayes (or a custom kernel for SVM, which enables them to work with nonnumeric data), which can work on any objects, as long as we can define perticular conditional probabilities - here, the most basic approach is treating document containing a particular word or not as a "feature". In general this is not the only possibility, there are dozens of methods that use statistical features, semantic features (based on some ontologies like wordnet) etc.
To sum up - this is only one, most simple representation of document for the machine learning model. Good to start with, good to understand the basics, but far from being a "feature definition".
EDIT
no_of_features is size of the vector you use for your documents' representation, so if you use tf-idf, then size of resulting vecor is a no_of_featuers.
I want to training data and use HOG algorithm to detect pedestrian.
Now I can use defaultHog.setSVMDetector(HOGDescriptor::getDefaultPeopleDetector()); in opencv to detection, but the result is not very good to my testing video. So I want to do training use my database.
I have prepared 1000+ positive sample, and 1000+ negative samples. They are cropped to size 50 * 100, and I have do the list file.
And I have read some tutorials on the internet, they all so complex, sometimes abstruse. Most of them are analyze the source code and the algorithm of HOG. But with only less examples and simple anylize.
Some instruction show that libsvm\windows\svm-train.exe can be used to training, Can anyone gives an examples according to 1000+ 50*100 positive samples?
For example, like haartraing, we can do it from opencv, like haartraining.exe –a –b with some parameters, and get a *.xml as a result which will be used to people detection?
Or is there any other method to training, and detection?
I prefer to know how to use it and the detail procedures. As the detail algorithm, it is not important to me. I just want to implement it.
If anyone know about it, please give me some tips.
I provided some sample code and instructions to start training your own HOG descriptor using openCV:
See https://github.com/DaHoC/trainHOG/wiki/trainHOG-Tutorial.
The algorithm is indeed too complex to provide in short, the basic idea however is to:
Extract HOG features from negative and positive sample images of identical size and type.
Use the extracted feature vectors along with their respective classes to train a SVM classifier, in this step you can use the svm-train.exe with a generated file of the correct format containing the feature vectors and their classes (or directly include and address the libsvm library class in your sources).
The resulting SVM model and support vectors are calculated into a single descriptor vector that can be used with the openCV detector.
Best regards
I am developing an application to read the letters and numbers from an image using opencv in c++. I first changed the given colour image and colour template to binary image, then called the method cvMatchTemplate(). This method just highlighted the areas where the template matches.. But not clear.. I just dont want to see the area.. I need to parse the characters(letters & numbers) from the image. I am new to openCV. Does anybody know any other method to get the result??
Image is taken from camera. the sample image is shown above. I need to get all the texts from the LED display(130 and Delft Tanthaf).
Friends I tried with the sample application of face detection, It detects the faces. the HaarCascade file is provided with the openCV. I just loaded that file and called the method cvHaarDetectObjects(); To detect the letters I created the xml file by using the application letter_recog.cpp provided by openCV. But when I loading this file, it shows some error(OpenCV error: UnSpecified error > in unknown function, file ........\ocv\opencv\src\cxcore\cxpersistence.cpp,line 4720). I searched in web for this error and got the information about lib files used. I did so, but the error still remains. Is the error with my xml file or calling the method to load this xml file((CvHaarClassifierCascade*)cvLoad("builded xml file name",0,0,0);)?? please HELP...
Thanks in advance
As of OpenCV 3.0 (in active dev), you can use the built-in "scene text" object detection module ~
Reference: http://docs.opencv.org/3.0-beta/modules/text/doc/erfilter.html
Example: https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/textdetection.cpp
The text detection is built on these two papers:
[Neumann12] Neumann L., Matas J.: Real-Time Scene Text Localization
and Recognition, CVPR 2012. The paper is available online at
http://cmp.felk.cvut.cz/~neumalu1/neumann-cvpr2012.pdf
[Gomez13] Gomez L. and Karatzas D.: Multi-script Text Extraction from
Natural Scenes, ICDAR 2013. The paper is available online at
http://refbase.cvc.uab.es/files/GoK2013.pdf
Once you've found where the text in the scene is, you can run any sort of standard OCR against those slices (Tesseract OCR is common). And there's now an end-to-end sample in opencv using OpenCV's new interface to Tesseract:
https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/end_to_end_recognition.cpp
Template matching tend not to be robust for this sort of application because of lighting inconsistencies, orientation changes, scale changes etc. The typical way of solving this problem is to bring in machine learning. What you are trying to do by training your own boosting classifier is one possible approach. However, I don't think you are doing the training correctly. You mentioned that you gave it 1 logo as a positive training image and 5 other images not containing the logo as negative examples? Generally you need training samples to be in the order of hundreds or thousands or more. You cannot possibly train with 6 training samples and expect it to work.
If you are unfamiliar with machine learning, here is roughly what you should do:
1) You need to collect many positive training samples (from hundred onwards but generally the more the merrier) of the object you are trying to detect. If you are trying to detect individual characters in the image, then get cropped images of individual characters. You can start with the MNIST database for this. Better yet, to train the classifier for your particular problem, get many cropped images of the characters on the bus from photos. If you are trying to detect the entire rectangular LED board panel, then use images of them as your positive training samples.
2) You will need to collect many negative training samples. Their number should be in the same order as the number of positive training samples you have. These could be images of the other objects that appear in the images you will run your detector on. For example, you could crop images of the front of the bus, road surfaces, trees along the road etc. and use them as negative examples. This is to help the classifier rule out these objects in the image you run your detector on. Hence, negative examples are not just any image containing objects you don't want to detect. They should be objects that could be mistaken for the object you are trying to detect in the images you run your detector on (at least for your case).
See the following link on how to train the cascade of classifier and produce the XML model file: http://note.sonots.com/SciSoftware/haartraining.html
Even though you mentioned you only want to detect the individual characters instead of the entire LED panel on the bus, I would recommend first detecting the LED panel so as to localize the region containing the characters of interest. After that, either perform template matching within this smaller region or run a classifier trained to recognize individual characters on patches of pixels in this region obtained using sliding window approach, and possibly at multiple scale. (Note: The haarcascade boosting classifier you mentioned above will detect characters but it won't tell you which character it detected unless you only train it to detect that particular character...) Detecting characters in this region in a sliding window manner will give you the order the characters appear so you can string them into words etc.
Hope this helps.
EDIT:
I happened to chance upon this old post of mine after separately discovering the scene text module in OpenCV 3 mentioned by #KaolinFire.
For those who are curious, this is the result of running that detector on the sample image given by the OP. Notice that the detector is able to localize the text region, even though it returns more than one bounding box.
Note that this method is not foolproof (at least this implementation in OpenCV with the default parameters). It tends to generate false-positives, especially when the input image contains many "distractors".
Here are more examples obtained using this OpenCV 3 text detector on the Google Street View dataset:
Notice that it has a tendency to find "text" between parallel lines (e.g., windows, walls etc). Since the OP's input image is likely going to contain outdoor scenes, this will be a problem especially if he/she does not restrict the region of interest to a smaller region around the LED signs.
It seems that if you are able to localize a "rough" region containing just the text (e.g., just the LED sign in the OP's sample image), then running this algorithm can help you get a tighter bounding box. But you will have to deal with the false-positives though (perhaps discarding small regions or picking among the overlapping bounding boxes using a heuristic based on knowledge about the way letters appear on the LED signs).
Here are more resources (discussion + code + datasets) on text detection.
Code
Extracting text OpenCV
http://libccv.org/doc/doc-swt/
Stroke Width Transform (SWT) implementation (Python)
https://github.com/subokita/Robust-Text-Detection
Datasets
You will find the google streetview and MSRA datasets here. Although the images in these datasets are not exactly the same as the ones for the LED signs on buses, they may be helpful either for picking the "best" performing algorithm from among several competing algorithms, or to train a machine learning algorithm from scratch.
http://www.iapr-tc11.org/mediawiki/index.php/Datasets_List
See my answer to How to read time from recorded surveillance camera video? You can/should use cvMatchTemplate() to do that.
If you are working with a fixed set of bus destinations, template matching will do.
However, if you want the system to be more flexible, I would imagine you would need some form of contour/shape analysis for each individual letter.
You can also look at EAST: Efficient Scene Text Detector - https://www.learnopencv.com/deep-learning-based-text-detection-using-opencv-c-python/
Under this link, you have examples with C++ and Python. I used this code to detect numbers of buses (after detecting that given object is a bus).