currently I am struggling in implementing an algorithm to locate an object in an image. Suppose I have 100 training sets that all have a cat in there and each has the correct coordinate of each cat. My first idea is to create a fixed-sized square and traverse through the image. For each collection of pixels contained in the square, we can use it as a point for Support Vector Machine algorithm.
The problem is that I am not sure how to do that since usually, each point represents each class (has object or has no object) and it usually has a simple d features, while for this case, it has a dx3 matrix as its features (each features has RGB value).
A simple help will be welcome, thanks!
If I have understood your question well, applying machine learning for image Processing and computer vision is a bit different from Other kinds of problems. main difference is that you should somehow overcome the issue of locality and scale. does all kitties always appear in the specific coordinate (x,y) ?! of course not! they can be anywhere in the scene. so how is it possible to give a specific point to SVM for an object? it will not be generalized at all. This is the reason almost all basic operation in computer vision has something to do with convolution operation to extract features independent from their location. A pixel alone carries zero useful information, you need to analyse groups of pixels. there are 2 approach you can take:
classic methods:
use OpenCV to perform noise removal, edge detection, feature extraction using methods like SIFT and feed those features to a model like SVM, not the raw unprocessed pixels. feature extraction means to reach from d features to k more meaningful representation of inputs where usually (k < d) if not always.
Deep Learning:
Convolutional Neural networks(CNNs) Have shed lights on many Computer vision tasks which were far beyond reach until recently and more importantly with frameworks like Keras and tensorflow most problems in computer vision is just programming tasks to be honest and doesn't require much knowledge as one needed before. because (CNNs) extract features themselves and you don't need to do the feature engineering anymore which requires a well educated and knowledgeable person on the task.
so, choose whatever method you see fit for kitty detection =^.^= .
Related
I'm trying to implement a face recognition algorithm using Python. I want to be able to receive a directory of images, and compute pair-wise distances between them, when short distances should hopefully correspond to the images belonging to the same person. The ultimate goal is to cluster images and perform some basic face identification tasks (unsupervised learning).
Because of the unsupervised setting, my approach to the problem is to calculate a "face signature" (a vector in R^d for some int d) and then figure out a metric in which two faces belonging to the same person will indeed have a short distance between them.
I have a face detection algorithm which detects the face, crops the image and performs some basic pre-processing, so the images i'm feeding to the algorithm are gray and equalized (see below).
For the "face signature" part, I've tried two approaches which I read about in several publications:
Taking the histogram of the LBP (Local Binary Pattern) of the entire (processed) image
Calculating SIFT descriptors at 7 facial landmark points (right of mouth, left of mouth, etc.), which I identify per image using an external application. The signature is the concatenation of the square root of the descriptors (this results in a much higher dimension, but for now performance is not a problem).
For the comparison of two signatures, I'm using OpenCV's compareHist function (see here), trying out several different distance metrics (Chi Square, Euclidean, etc).
I know that face recognition is a hard task, let alone without any training, so I'm not expecting great results. But all I'm getting so far seems completely random. For example, when calculating distances from the image on the far right against the rest of the image, I'm getting she is most similar to 4 Bill Clintons (...!).
I have read in this great presentation that it's popular to carry out a "metric learning" procedure on a test set, which should significantly improve results. However it does say in the presentation and elsewhere that "regular" distance measures should also get OK results, so before I try this out I want to understand why what I'm doing gets me nothing.
In conclusion, my questions, which I'd love to get any sort of help on:
One improvement I though of would be to perform LBP only on the actual face, and not the corners and everything that might insert noise to the signature. How can I mask out the parts which are not the face before calculating LBP? I'm using OpenCV for this part too.
I'm fairly new to computer vision; How would I go about "debugging" my algorithm to figure out where things go wrong? Is this possible?
In the unsupervised setting, is there any other approach (which is not local descriptors + computing distances) that could work, for the task of clustering faces?
Is there anything else in the OpenCV module that maybe I haven't thought of that might be helpful? It seems like all the algorithms there require training and are not useful in my case - the algorithm needs to work on images which are completely new.
Thanks in advance.
What you are looking for is unsupervised feature extraction - take a bunch of unlabeled images and find the most important features describing these images.
The state-of-the-art methods for unsupervised feature extraction are all based on (convolutional) neural networks. Have look at autoencoders (http://ufldl.stanford.edu/wiki/index.php/Autoencoders_and_Sparsity) or Restricted Bolzmann Machines (RBMs).
You could also take an existing face detector such as DeepFace (https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf), take only feature layers and use distance between these to group similar faces together.
I'm afraid that OpenCV is not well suited for this task, you might want to check Caffe, Theano, TensorFlow or Keras.
I am right now in a serious problem, I need to compare images of flowers (carnations) using a genetic algorithm, the program must determine which variety does the flower belongs to (until now I am using 15 different varieties), the thing is I am having difficulties constructing the chromosome, right know I am only analysing the HSV of each image, then a take every channel and calculate the mean for each (n=255), after that I calculate the correlation between HS, HV and SV, I expected that the mean would be enough to locate any new flower next to the clusters of flowers of the variety it belongs (by the way, I have a database of all the flowers used for training purpose) by calculating the distance between the mean of the flower and the centroids of each cluster, and probably using the correlations for adjustment, but that distance is usually way smaller to a different variety than the one it must be. Is there a way to classify this flowers using ONLY colours (I've read of applications that uses the texture, but that's way out of my league), especially using a genetic algorithm (I know Neural Networks are more appropriate to this kind of analysis but that's what the teacher asked)?. Thank you very much. By the way I am working on OpenCV, don’t know if it's relevant. PS: Excuse my English if any mistakes were done, not my native language.
I am currently working on a Sign Language Recognition application, where I would like to use a Hidden Markov Model as the classification stage, meaning that I will classify a gesture/posture to obtain the relevant letter or word.
I have currently completed the first stage where I am detecting the hand. Currently I can obtain a number of parameters (features) which I can use for my machine learning stage such as:
convex hull of hand
convexity defects
centroid of the hand
bounding rotated ellipses/rectangles (e.g. obtain any angle needed in terms of rotation)
contour of the hand
moments (I am not sure what these are extactly)
These are all possible to do through openCv.
My question: once I have all these features, how can I execute the 'Feature Extraction' stage? i.e. if a machine learning algorithm, in this case the HMM requires a set of probabilities, how can I use the above information?
One idea I have is to create a special data structure with such information which uniquely identifies each gesture, but how do I feed it to the machine learning technique? (in this case the Hidden Markov Model)
Can any one be able to guide me as to what I should at least search for at this particular stage or guide me to show what is actually the real difficulty I have?
Once you have your set of observations ready, you could feed it to the Viterbi Algorithm to detect the best state sequence that may have produced these observations. Also, you can train your HMM over a data set of samples using the Baum-Welch algorithm. You could have a look at my blog post which is a simple explanation of recognizing dynamic hand gestures using HMM (although I am NOT using openCV or scanning the contour of the hand). Hope this can help you in getting a general idea about the processing and learning phase.
I know that most common object detection involves Haar cascades and that there are many techniques for feature detection such as SIFT, SURF, STAR, ORB, etc... but if my end goal is to recognizes objects doesn't both ways end up giving me the same result? I understand using feature techniques on simple shapes and patterns but for complex objects these feature algorithms seem to work as well.
I don't need to know the difference in how they function but whether or not having one of them is enough to exclude the other. If I use Haar cascading, do I need to bother with SIFT? Why bother?
thanks
EDIT: for my purposes I want to implement object recognition on a broad class of things. Meaning that any cups that are similarly shaped as cups will be picked up as part of class cups. But I also want to specify instances, meaning a NYC cup will be picked up as an instance NYC cup.
Object detection usually consists of two steps: feature detection and classification.
In the feature detection step, the relevant features of the object to be detected are gathered.
These features are input to the second step, classification. (Even Haar cascading can be used
for feature detection, to my knowledge.) Classification involves algorithms
such as neural networks, K-nearest neighbor, and so on. The goal of classification is to find
out whether the detected features correspond to features that the object to be detected
would have. Classification generally belongs to the realm of machine learning.
Face detection, for example, is an example of object detection.
EDIT (Jul. 9, 2018):
With the advent of deep learning, neural networks with multiple hidden layers have come into wide use, making it relatively easy to see the difference between feature detection and object detection. A deep learning neural network consists of two or more hidden layers, each of which is specialized for a specific part of the task at hand. For neural networks that detect objects from an image, the earlier layers arrange low-level features into a many-dimensional space (feature detection), and the later layers classify objects according to where those features are found in that many-dimensional space (object detection). A nice introduction to neural networks of this kind is found in the Wolfram Blog article "Launching the Wolfram Neural Net Repository".
Normally objects are collections of features. A feature tends to be a very low-level primitive thing. An object implies moving the understanding of the scene to the next level up.
A feature might be something like a corner, an edge etc. whereas an object might be something like a book, a box, a desk. These objects are all composed of multiple features, some of which may be visible in any given scene.
Invariance, speed, storage; few reasons, I can think on top of my head. The other method to do would be to keep the complete image and then check whether the given image is similar to glass images you have in your database. But if you have a compressed representation of the glass, it will need lesser computation (thus faster), will need lesser storage and the features tells you the invariance across images.
Both the methods you mentioned are essentially the same with slight differences. In case of Haar, you detect the Haar features then you boost them to increase the confidence. Boosting is nothing but a meta-classifier, which smartly chooses which all Harr features to be included in your final meta-classification, so that it can give a better estimate. The other method, also more or less does this, except that you have more "sophisticated" features. The main difference is that, you don't use boosting directly. You tend to use some sort of classification or clustering, like MoG (Mixture of Gaussian) or K-Mean or some other heuristic to cluster your data. Your clustering largely depends on your features and application.
What will work in your case : that is a tough question. If I were you, I would play around with Haar and if it doesn't work, would try the other method (obs :>). Be aware that you might want to segment the image and give some sort of a boundary around for it to detect glasses.
for my final thesis i am trying to build up an 3d face recognition system by combining color and depth information. the first step i did, is to realign the data-head to an given model-head using the iterative closest point algorithm. for the detection step i was thinking about using the libsvm. but i dont understand how to combine the depth and the color information to one feature vector? they are dependent information (each point consist of color (RGB), depth information and also scan quality).. what do you suggest to do? something like weighting?
edit:
last night i read an article about SURF/SIFT features i would like to use them! could it work? the concept would be the following: extracting this features out of the color image and the depth image (range image), using each feature as a single feature vector for the svm?
Concatenation is indeed a possibility. However, as you are working on 3d face recognition you should have some strategy as to how you go about it. Rotation and translation of faces will be hard to recognize using a "straightforward" approach.
You should decide whether you attempt to perform a detection of the face as a whole, or of sub-features. You could attempt to detect rotation by finding some core features (eyes, nose, etc).
Also, remember that SVMs are inherently binary (i.e. they separate between two classes). Depending on your exact application you will very likely have to employ some multi-class strategy (One-against-all or One-against-many).
I would recommend doing some literature research to see how others have attacked the problem (a google search will be a good start).
It sounds simple, but you can simply concatenate the two vectors into one. Many researchers do this.
What you arrived at is an important open problem. Yes, there are some ways to handle it, as mentioned here by Eamorr. For example you can concatenate and do PCA (or some non linear dimensionality reduction method). But it is kind of hard to defend the practicality of doing so, considering that PCA takes O(n^3) time in the number of features. This alone might be unreasonable for data in vision that may have thousands of features.
As mentioned by others, the easiest approach is to simply combine the two sets of features into one.
SVM is characterized by the normal to the maximum-margin hyperplane, where its components specify the weights/importance of the features, such that higher absolute values have a larger impact on the decision function. Thus SVM assigns weights to each feature all on its own.
In order for this to work, obviously you would have to normalize all the attributes to have the same scale (say transform all features to be in the range [-1,1] or [0,1])