I have written an object classification program using BoW clustering and SVM classification algorithms. The program runs successfully. Now that I can classify the objects, I want to track them in real time by drawing a bounding rectangle/circle around them. I have researched and came with the following ideas.
1) Use homography by using the train set images from the train data directory. But the problem with this approach is, the train image should be exactly same as the test image. Since I'm not detecting specific objects, the test images are closely related to the train images but not essentially an exact match. In homography we find a known object in a test scene. Please correct me if I am wrong about homography.
2) Use feature tracking. Im planning to extract the features computed by SIFT in the test images which are similar to the train images and then track them by drawing a bounding rectangle/circle. But the issue here is how do I know which features are from the object and which features are from the environment? Is there any member function in SVM class which can return the key points or region of interest used to classify the object?
Thank you
Related
My intention is to build a classifier that correctly classify the image ROI with the template that I have manually extracted
Here is what I have done.
My first step is to understand what should be done to achieve the above
I have realized I would need to create the representation vectors(of the template) through research from the net. Hence I have used Bag of words to create the vocabulary
I have used and rewritten the Roy's project to opencv 3.1 and also used his food database. On seeing his database, I have realised that some of the image contain multiple class type. I try to clip the image so that each training image only contains one class of item but the image are now of different size
I have tried to run this code. The result is very disappointing. It always points to one class.
Question I have?
Is my step in processing the training image wrong? I read around and some posts suggest the image size must be constant or at least the aspect ratio. I am confused by this. Is there some tools available for resizing samples?
It does not matter what is the size of the sample images, since Roy's algorithm uses local descriptors extracte from nearby points of interest.
SVM is linear regression classifier and you need to train different SVM-s for each class. For each class it will say whether it's of that class or the rest. The so called one vs. rest.
After obtaining the image dataset, the feature database is constructed for all images which is a vector based on mean and sd of RGB color model and HSV color model for a portion of the image. How can I use a svm to retieve related images from the database once the query image is given.
Also how to use unsupervised learning for the above problem
Assuming the query images are unlabeled, applying SVM would require a way of knowing the labels for dataset images since SVM is a form of supervised learning, which seeks to correctly determine class labels for unlabeled data. You would need another method for generating class labels, such as unsupervised learning, so this approach does not seem relevant if you only have feature vectors but no class labels.
A neural network allows for unsupervised learning with unlabeled data, but is a rather complex approach and is the subject of academic research. You may want to consider a simpler machine learning approach such as k-Nearest Neighbors, which allows you to obtain the k closest training samples that are similar in your feature space. This algorithm is simple to implement and is found in many machine learning libraries. For example in Python you can use scikit learn.
I am unsure what type of images you are working with, but you might also want to explore using feature detector algorithms such as SIFT rather than just pixel intensities.
I have been tasked to use OpenCV and C++
Read a set of videos for creating a set of images/learning.
Classify objects seen in the videos
Label the images
test against series of test videos to check objects were identified as expected. draw a rectangle around them and label.
I am new to OpenCV however happy to program in C++ as soon as approach is formed. I am also planning to write my own functions at a later stage.
I need your help in formning right way of solution approach as I have to identify household objects [cup, soft toy, phone, camera, keyboard) from a stream of video and then test on another stream of video. The original video has depth information as well but not sure how to use it to my benefit.
Read about Support vector machine (SVM) , Feature extraction (e.g. SIFT/SURF) , SVM training and SVM testing. And, for drawing Rectangle, read about findContour(), drawContour() in openCV.
Approach:
Detect objects (e.g. car/plane etc.). Store the points of its contours
Extract some features of that object using SIFT/SURF
Based upon the extracted features, classify the object using SVM (the input for SVM will be the extracted features)
And if the SVM says -Yes! it is a car. Then, draw a rectangle around it using the points of its contour which you had stored in first step.
I am trying to develop an automatic(or semi-automatic) image annotator for my final year project with OpenCV. I have been studying many OpenCV resources and have come across cascade classification for training and detection purposes. I understood that part, and also tried the Face Detection tutorial provided with OpenCV. So, now I know how to train and detect objects.
However, I still cannot understand how can I annotate objects present in the image?
For example, the system will show that this is an object, but I want the system to show that it is a ball. How can i accomplish that?
Thanks in advance.
One binary classificator (detector) can separate objects by two classes:
positive - the object type classifier was trained for,
and negative - all others.
If you need detect several distinguished classes you should use one detector for each class, or you can train multiclass classifier ("one vs all" type of classifiers for example), but it usually works slower and with less accuracy (because detector better search for similar objects). You can also take a look at convolutional networks (by Yann LeCun).
This is a very hard task. I suggest simplifying it by using latent SVM detector and limiting yourself to the models it supplies:
http://docs.opencv.org/modules/objdetect/doc/latent_svm.html
in my project i want to identify objects and then detect what they are(water bottle, ball, etc).
I thought of identifying the objects in the image and then match that object with a object database using SURF method.
But the problem is to identify the whether my image has a object or not and how many objects are there.
I did some search and find out about "contours", a way to track shapes. I want to know whether "contours" will help to solve my problem or any other way to solve this.
Thanks.
First, for identifying the objects you can also use BoW, cascade classifier or latent svm.
Once you have an object classifier, you can use the sliding window approach to search for the object in the image. Take a look at the cascade classifier for an example of the sliding window approach.
EDIT: here's a post blog I wrote about BoW theory and packages in Matlab and openCV
http://gilscvblog.wordpress.com/2013/08/23/bag-of-words-models-for-visual-categorization/