I would like to create an application which can learn to classify a sequence of points drawn by a user, e.g. something like handwriting recognition. If the data point consists of a number of (x,y) pairs (like the pixels corresponding to a gesture instance), what are the best features to compute about the instance which would make for a good multi-class classifier (e.g. SVM, NN, etc)? Particularly if there are limited training examples provided.
If I were you, I would find the data points that correspond with corners, end points and intersections, use those as features and discard the intermediate points. You could include the angle or some other descriptor of these interest points as well.
For detecting interest points you could use a Harris detector, you could then use the gradient value at that point as a simple descriptor. Alternatively you could go with a more fancy method like SIFT.
You could use the descriptor of every pixel in your downsampled image and then classify with SVM. The disadvantage of that is that there would be a large amount of uninteresting data points in the feature vector.
An alternative would be to not approach it as a classification problem but as a template matching problem (fairly common in computer-vision). In this case a gesture can be specified as an arbitrary number of interest points, completely leaving out the non-interesting data. A certain threshold percentage of an instance's points has to match a template for a positive identification. For example, when matching the corner points of an instance of 'R' against the template for 'X', the bottom right point should match, being end points in the same position orientation, but the others are too dissimilar, giving a fairly low score and the identification R=X will be rejected.
Related
As we know, faster-RCNN has two main parts: one is region proposal network(RPN), and another one is fast-RCNN.
My question is, now that region proposal network(RPN) can output class scores and bounding boxes and is trainable, why do we need Fast-RCNN?
Am I thinking it right that the RPN is enough for detection (red circle), and Fast-RCNN is now becoming redundant (blue circle)?
Short answer: no they are not redundant.
The R-CNN article and its variants popularized the use of what we used to call a cascade.
Back then for detection it was fairly common to use different detectors often very similar in structures to do detection because of their complementary power.
If the detections are partly orthogonal it allows to remove false positive along the way.
Furthermore by definition both parts of R-CNN have different roles the first one is used to discriminate objects from background and the second one to discriminate fine grained categories of objects from themselves (and from the background also).
But you are right if there is only 1 class vs the background one could use only the RPN part to to detection but even in that case it would probably better the result to chain two different classifiers (or not see e.g. this article)
PS: I answered because I wanted to but this question is definitely unsuited for stackoverflow
If you just add a class head to the RPN Network, you would indeed get detections, with scores and class estimates.
However, the second stage is used mainly to obtain more accurate detection boxes.
Faster-RCNN is a two-stage detector, like Fast R-CNN.
There, Selective Search was used to generate rough estimates of the location of objects and the second stage then refines them, or rejects them.
Now why is this necessary for the RPN? So why are they only rough estimates?
One reason is the limited receptive field:
The input image is transformed via a CNN into a feature map with limited spatial resolution. For each position on the feature map, the RPN heads estimate if the features at that position correspond to an object and the heads regress the detection box.
The box regression is done based on the final feature map of the CNN. In particular, it may happen that the correct bounding box on the image is larger than the corresponding receptive field due to the CNN.
Example: Lets say we have an image depicting a person and the features at one position of the feature map indicate a high possibiliy for the person. Now, if the corresponding receptive field contains only the body parts, the regressor has to estimate a box enclosing the entire person, although it "sees" only the body part.
Therefore, RPN creates a rough estimate of the bounding box. The second stage of Faster RCNN uses all features contained in the predicted bounding box and can correct the estimate.
In the example, RPN creates a too large bounding box, which is enclosing the person (since it cannot the see the pose of the person), and the second stage uses all information of this box to reshape it such that it is tight. This however can be done much more accurate, since more content of the object is accessable for the network.
faster-rcnn is a two-stage method comparing to one stage method like yolo, ssd, the reason faster-rcnn is accurate is because of its two stage architecture where the RPN is the first stage for proposal generation and the second classification and localisation stage learn more precise results based on the coarse grained result from RPN.
So yes, you can, but your performance is not good enough
I think the blue circle is completely redundant and just adding a class classification layer (gives class for each bounding box containing object) should work just fine and that's what the single shot detectors do with compromised accuracy.
According to my understanding, RPN is just for binary checking if you have Objects in the bbox or not and final Detector part is for classifying the classes ex) car, human, phones, etc
I have go question while programming K-means algorithm in Matlab. Why K-means algorithm not suitable for classifying elongated data set?
In sort, draw some thick lines on a paper. Can you really represent each one with a single point? How would single points give information about orientation?
K-means assigns each datapoint to each nearest centroid. That is to say that for each centroid c, all points that their distance from c is smaller (in comparison to all other centroids) will be assigned to c. And, since the surface of a (hyper)sphere is in fact, all points with distance less or equal to some value from a center, I think it is easy to see how resulted clusters tend to be spherical. (To be exact, kmeans practically creates a Voronoi diagram in the vector space)
Elongated clusters however, don't necessarily satisfy the requirement that all their points are closer to their "center of mass" than to some other cluster's center.
It is difficult for you to choose a init cluster center point in elongated data set, but it has a powerful effect on the result.You may get different results when choose different points.
You will get only one result in this case when you choose 3 init points:
But it is different in elongated data set.
Is there any way to reduce the dimension of the following features from 2D coordinate (x,y) to one dimension?
Yes. In fact, there are infinitely many ways to reduce the dimension of the features. It's by no means clear, however, how they perform in practice.
A feature reduction usually is done via a principal component analysis (PCA) which involves a singular value decomposition. It finds the directions with highest variance -- that is, those direction in which "something is going on".
In your case, a PCA might find the black line as one of the two principal components:
The projection of your data onto this one-dimensional subspace than yields the reduced form of your data.
Already with the eye one can see that on this line the three feature sets can be separated -- I coloured the three ranges accordingly. For your example, it is even possible to completely separate the data sets. A new data point then would be classified according to the range in which its projection onto the black line lies (or, more generally, the projection onto the principal component subspace) lies.
Formally, one could obtain a division with further methods that use the PCA-reduced data as input, such as for example clustering methods or a K-nearest neighbour model.
So, yes, in case of your example it could be possible to make such a strong reduction from 2D to 1D, and, at the same time, even obtain a reasonable model.
I am trying to implement Canny edge detection found hereCanny edge to differentiate objects based on their shapes. I would like to know what are the features? I need to find a score/metric so that I can define a probability from information like mean of the shape. The purpose is to differentiate between objects of different shapes. So, lets assume that the mean shape(x) of Object1 and Object2 are x1,x2 and the standard deviation(s) is s1,s2 respectively. From what do I calculate these information and How do I find these information?
Canny Algorithm is an edge detector. It searches for high frequencies in the image by computing the magnitude of the derivatives in x and y direction. In the end of you have contours of objects. What you are trying to do is to classify objects and using Canny does not sound like a right way to do it, I am not saying you cannot build features out of edges, but it might perform poorly.
In order to achieve what you want, you need first to identify what features are important for you. You mentioned the shape but is the color a good feature for the class of objects you are trying to find? Your pictures show very colorful objects. Are you only trying to distinguish one object to the other (considering the images only display only the object of interest) or do you want locate them in the screen? Does the image contain only one object or multiple ones?
I will give you some direction regarding feature modeling.
If color is a strong information for your objects, you could model your features using histogram information, compute n bins for all objects and store the distribution of the bins as a feature vector. You can use HOG.
Another possible (naive) solution is to compute all colors of patches (e.g. 7x7) belonging to each object and to compute later the histogram over patches instead of single pixels.
If you are not satisfied with color information and you would like to differentiate objects by comparing information in their neighborhood, you can use local binary patterns, which might be enough for the type of information you have.
Once you decide on the features which are important and modeled them, you can go for the classification (which is gonna determine which object you are seeing given a certain feature).
A probabilistic framework tries to estimate the posterior probability P(X|C), i.e. what is the probability of being object X given that we observed C (C could be your feature) and this is very powerful. You might consider reading about Maximum Likelihood Estimation and Maximum a posteriori. Also, a Naive Bayes classifier is a simple off the shelf algorithm available on Opencv that you could use.
You could use many other algorithms, such as SVM, Boost, Decision Trees, Neural Networks and so on. Bag of visual words is also a nice alternative.
If you are interested how to separate the object of interest from the background you are talking about image segmentation, you can look at K-Means or more powerfully Graph Cuts techniques. Of course you can always segment first and then classify the segmented blobs.
Samuel
I've been working on a project for some time, to detect and track (moving) vehicles in video captured from UAV's, currently I am using an SVM trained on bag-of-feature representations of local features extracted from vehicle and background images. I am then using a sliding window detection approach to try and localise vehicles in the images, which I would then like to track. The problem is that this approach is far to slow and my detector isn't as reliable as I would like so I'm getting quite a few false positives.
So I have been considering attempting to segment the cars from the background to find the approximate position so to reduce the search space before applying my classifier, but I am not sure how to go about this, and was hoping someone could help?
Additionally, I have been reading about motion segmentation with layers, using optical flow to segment the frame by flow model, does anyone have any experience with this method, if so could you offer some input to as whether you think this method would be applicable for my problem.
Below is two frames from a sample video
frame 0:
frame 5:
Assumimg your cars are moving, you could try to estimate the ground plane (road).
You may get a descent ground plane estimate by extracting features (SURF rather than SIFT, for speed), matching them over frame pairs, and solving for a homography using RANSAC, since plane in 3d moves according to a homography between two camera frames.
Once you have your ground plane you can identify the cars by looking at clusters of pixels that don't move according to the estimated homography.
A more sophisticated approach would be to do Structure from Motion on the terrain. This only presupposes that it is rigid, and not that it it planar.
Update
I was wondering if you could expand on how you would go about looking for clusters of pixels that don't move according to the estimated homography?
Sure. Say I and K are two video frames and H is the homography mapping features in I to features in K. First you warp I onto K according to H, i.e. you compute the warped image Iw as Iw( [x y]' )=I( inv(H)[x y]' ) (roughly Matlab notation). Then you look at the squared or absolute difference image Diff=(Iw-K)*(Iw-K). Image content that moves according to the homography H should give small differences (assuming constant illumination and exposure between the images). Image content that violates H such as moving cars should stand out.
For clustering high-error pixel groups in Diff I would start with simple thresholding ("every pixel difference in Diff larger than X is relevant", maybe using an adaptive threshold). The thresholded image can be cleaned up with morphological operations (dilation, erosion) and clustered with connected components. This may be too simplistic, but its easy to implement for a first try, and it should be fast. For something more fancy look at Clustering in Wikipedia. A 2D Gaussian Mixture Model may be interesting; when you initialize it with the detection result from the previous frame it should be pretty fast.
I did a little experiment with the two frames you provided, and I have to say I am somewhat surprised myself how well it works. :-) Left image: Difference (color coded) between the two frames you posted. Right image: Difference between the frames after matching them with a homography. The remaining differences clearly are the moving cars, and they are sufficiently strong for simple thresholding.
Thinking of the approach you currently use, it may be intersting combining it with my proposal:
You could try to learn and classify the cars in the difference image D instead of the original image. This would amount to learning what a car motion pattern looks like rather than what a car looks like, which could be more reliable.
You could get rid of the expensive window search and run the classifier only on regions of D with sufficiently high value.
Some additional remarks:
In theory, the cars should even stand out if they are not moving since they are not flat, but given your distance to the scene and camera resolution this effect may be too subtle.
You can replace the feature extraction / matching part of my proposal with Optical Flow, if you like. This amounts to identifying flow vectors that "stick out" from a consistent frame-to-frame motion of the ground. It may be prone to outliers in the optical flow, however. You can also try to get the homography from the flow vectors.
This is important: Regardless of which method you use, once you have found cars in one frame you should use this information to robustify your search of these cars in consecutive frame, giving a higher likelyhood to detections close to the old ones (Kalman filter, etc). That's what tracking is all about!
If the number of cars in your field of view always remain the same but move around then you can use optical flow...it will give you good results against a still background...if the number of cars are changing then you need to call goodFeaturestoTrack function in OpenCV after certain number of frames and again track the cars using optical flow.
You can use background modelling to model the background and hence the cars are always your foreground.The simplest example is frame differentiation...subtract the previous frame current frame. diff(x,y,k) = I(x,y,k) - I(x,y,k-1) .As your cars are moving in each frame you will get their position..
Both the process will work fine since you have a still background I presume..check this link to find what Optical flow can do.