Most of the approaches to detection problems are based more or less on some form of bounding box proposal, which turns to two class (positive/negative) classification. I found lots of materials on these topics.
But I was wondering, aren't there some approaches taking the whole image as an input, then sending it through several convolutional and pooling layers, output of which would be two numbers (x, y position of the object)? Of course this would mean that there's just one object in the image. So far I haven't find anything about this, should I consider it not usable?
Related
Shown above is a sample image of runway that needs to be localized(a bounding box around runway)
i know how image classification is done in tensorflow, My question is how do I label this image for training?
I want model to output 4 numbers to draw bounding box.
In CS231n they say that we use a classifier and a localization head.
but how does my model knows where are the runnway in 400x400 images?
In short How do I LABEL this image for training? So that after training my model detects and localizes(draw bounding box around this runway) runways from input images.
Please feel free to give me links to lectures, videos, github tutorials from where I can learn about this.
**********Not CS231n********** I already took that lecture and couldnt understand how to solve using their approach.
Thanks
If you want to predict bounding boxes, then the labels are also bounding boxes. This is what most object detection systems use for training. You can just have bounding box labels, or if you want to detect multiple object classes, then also class labels for each bounding box would be required.
Collect data from google or any resources that contains only runway photos (From some closer view). I would suggest you to use a pre-trained image classification network (like VGG, Alexnet etc.) and fine tune this network with downloaded runway data.
After building a good image classifier on runway data set you can use any popular algorithm to generate region of proposal from the image.
Now take all regions of proposal and pass them to classification network one by one and check weather this network is classifying given region of proposal as positive or negative. If it classifying as positively then most probably your object(Runway) is present in that region. Otherwise it's not.
If there are a lot of region of proposal in which object is present according to classifier then you can use non maximal suppression algorithms to reduce number of positive proposals.
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'm trying to use a pretrained VGG16 as an object localizer in Tensorflow on ImageNet data. In their paper, the group mentions that they basically just strip off the softmax layer and either toss on a 4D/4000D fc layer for bounding box regression. I'm not trying to do anything fancy here (sliding windows, RCNN), just get some mediocre results.
I'm sort of new to this and I'm just confused about the preprocessing done here for localization. In the paper, they say that they scale the image to 256 as its shortest side, then take the central 224x224 crop and train on this. I've looked all over and can't find a simple explanation on how to handle localization data.
Questions: How do people usually handle the bounding boxes here?...
Do you use something like the tf.sample_distorted_bounding_box command, and then rescale the image based on that?
Do you just rescale/crop the image itself, and then interpolate the bounding box with the transformed scales? Wouldn't this result in negative box coordinates in some cases?
How are multiple objects per image handled?
Do you just choose a single bounding box from the beginning ,crop to that, then train on this crop?
Or, do you feed it the whole (centrally cropped) image, and then try to predict 1 or more boxes somehow?
Does any of this generalize to the Detection or segmentation (like MS-CoCo) challenges, or is it completely different?
Anything helps...
Thanks
Localization is usually performed as an intersection of sliding windows where the network identifies the presence of the object you want.
Generalizing that to multiple objects works the same.
Segmentation is more complex. You can train your model on a pixel mask with your object filled, and you try to output a pixel mask of the same size
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 have been doing reading about Self Organizing Maps, and I understand the Algorithm(I think), however something still eludes me.
How do you interpret the trained network?
How would you then actually use it for say, a classification task(once you have done the clustering with your training data)?
All of the material I seem to find(printed and digital) focuses on the training of the Algorithm. I believe I may be missing something crucial.
Regards
SOMs are mainly a dimensionality reduction algorithm, not a classification tool. They are used for the dimensionality reduction just like PCA and similar methods (as once trained, you can check which neuron is activated by your input and use this neuron's position as the value), the only actual difference is their ability to preserve a given topology of output representation.
So what is SOM actually producing is a mapping from your input space X to the reduced space Y (the most common is a 2d lattice, making Y a 2 dimensional space). To perform actual classification you should transform your data through this mapping, and run some other, classificational model (SVM, Neural Network, Decision Tree, etc.).
In other words - SOMs are used for finding other representation of the data. Representation, which is easy for further analyzis by humans (as it is mostly 2dimensional and can be plotted), and very easy for any further classification models. This is a great method of visualizing highly dimensional data, analyzing "what is going on", how are some classes grouped geometricaly, etc.. But they should not be confused with other neural models like artificial neural networks or even growing neural gas (which is a very similar concept, yet giving a direct data clustering) as they serve a different purpose.
Of course one can use SOMs directly for the classification, but this is a modification of the original idea, which requires other data representation, and in general, it does not work that well as using some other classifier on top of it.
EDIT
There are at least few ways of visualizing the trained SOM:
one can render the SOM's neurons as points in the input space, with edges connecting the topologicaly close ones (this is possible only if the input space has small number of dimensions, like 2-3)
display data classes on the SOM's topology - if your data is labeled with some numbers {1,..k}, we can bind some k colors to them, for binary case let us consider blue and red. Next, for each data point we calculate its corresponding neuron in the SOM and add this label's color to the neuron. Once all data have been processed, we plot the SOM's neurons, each with its original position in the topology, with the color being some agregate (eg. mean) of colors assigned to it. This approach, if we use some simple topology like 2d grid, gives us a nice low-dimensional representation of data. In the following image, subimages from the third one to the end are the results of such visualization, where red color means label 1("yes" answer) andbluemeans label2` ("no" answer)
onc can also visualize the inter-neuron distances by calculating how far away are each connected neurons and plotting it on the SOM's map (second subimage in the above visualization)
one can cluster the neuron's positions with some clustering algorithm (like K-means) and visualize the clusters ids as colors (first subimage)