I am trying to build a binary classifier using Caffe. First I trained my own image dataset by slightly modifying Fine-tuning for style recognition model. But when I try to classify a single image, it gives 50% probability for both of the classes. To classify, I used the same deploy.prototxt, just changed output_number to 2. This is strange, since while training I get accuracy ~85%. I tried both python (classify.py) and cpp (classification.cpp), and both give same result. I think I am doing something wrong with the pipeline.
Thanks for your help.
Related
I'm looking for a way to check if a given logo appears on a screenshot of a webpage. So basically, I need to be able to find a small predefined image on a larger image that may or may not contain the smaller image. A match could be of a different scale, somewhat different colors. I need to judge occurrence similarity as well. Need some pointers for what to look at, I've never worked with computer vision before.
Simplest yet not simple way to do it is a normal CNN trained on augmented dataset of the logos.
Trying to keep the answer short, Just make a cnn in tensorflow and train your model on tons images of logos with labels on each training image, It's a simple task and a not-very-crafty CNN must be able to get your work done.
CNN- Convolutional Neural Network
Reference : https://etasr.com/index.php/ETASR/article/view/3919
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.
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
Say I want to use some other product to create an MLP (R,Python, Matlab, whatever) but I want to run that network, i.e. just for prediction, under opencv. Assume that the parameters (e.g. activation function) are compatible between the training product and opencv.
How can I import my trained weights into the opencv MLP? Perhaps the training product uses an MxN matrix of weights for each layer where M is the input layer and M the output (and so W(i,j) would be the weight between input node i and output node j.) Perhaps the biases are stored in a separate N element vector. The specifics of the original format don't matter so much because as long as I know what the weights mean and how they are stored I can transform them however opencv needs them.
So, given that, how do I import these weights into a (run time prediction only) opencv MLP? What weight bias (etc?) format does opencv need and how do I set its weights+baises?
I've just run into the same problem. I haven't looked at Opencv's MLP class enough yet to know if there's an easier/simpler way to, but OpenCV let's you save and load MLP's from .xmls and .ymls so if you make an ANN in OpenCV, you can then save it to one of those formats, look at it to figure out the format OpenCV wants, and then save your network into that format from R/Python/MatLab or at least into some format and make a script to translate it from there to OpenCv's format. Once you have that done it should be as simple as instantiating opencv's mlp in the code you actually want to use it to predict on and calling the load("filename") function on it. (I realize this is a year after the fact, so hopefully you found an answer or a work around. If you found a better idea, tell me, I'd love to know).
You must parse your model like how the 'read' function of MLP in OpenCV parse the xml or yml. I think this will not too hard.
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