Using Histogram of Ordered Gradients (HoG) I have computed features of 15 sample images. The feature vectors generated by these samples are very large (i.e. take up a lot of memory).
To reduce these feature vectors, i am using Principal Component Analysis (PCA). Here is the OpenCV code I am using:
PCA pca(imageT, Mat(), CV_PCA_DATA_AS_ROW, 300);
pca.project(imageT, imageT1);
In imageT Matrix, no.of row =no. of sample
no. of coloumns = no. of features
Suppose for 15 images
no.of row of imageT is 15 and no. of coloumn is 57400
I want 300 features after applying PCA; it gives me less than 15 features. I need help.
See also
OpenCV Documentation: PCA
Wikipedia Histogram of Ordered Gradients
Wikipedia: Principle Component Analysis
Selection of Histograms of Oriented GradientsFeatures for Pedestrian Detection
Kernel PCA of HOG features for posture detection
Related
I am having problem to understand about size of HOG feature vector...
scene: I took a 286x286 image.Then I calculated HOG for 8x8 patch each.Mean I got 8x8x2=128 numbers represented by a 9 bin histogram for each patch.so can I say this 9 bin histogram as a 9 dimensional vector?.After,total number of patch to estimate HOG in whole image was approx. 1225(since I have square matrix I estimated total patch by squaring(286/8)=35)).I iterated 1225 patches and calculated 9 bin histogram for each.(I didn't applied 16x16 block normalization) After that concatenating all vector together I obtained 1225x9=11,025 sized HOG of whole image.
question:
1.Then is is right to say I obtained 11,025 dimension of an HOG vector in given image?
2.Am I going in right direction?(if I opt for classification via neural network)
3.Is this concatenated HOG feature can directly feeded to PCA for dimension reduction?or need further more preprocessing?(in genral not in advance)
Thank you in advance!
Yes
Probably not. What are you trying to do? For example, if you are doing classification, you should use bag-of-words (actually, you should stop using HOG and try deep learning). If you are doing image retrieval/matching, you should compute HOG feature for local patches.
You can almost always use PCA for dimensionality reduction for your features, even for 128 dimensional SIFT.
1) In eigenface approach the eigenfaces is a combination of elements from different faces. What are these elements?
2) The output face is an image composed of different eigenfaces with different weights. What does the weights of eigenfaces exactly mean? I know that the weight is percentage of eigenfacein the image, but what does it mean exactly, is mean the number of selected pixels?
Please study about PCA to understand what is the physical meaning of eigenfaces, when PCA is applied to an image. The answer lies in the understanding of eigenvectors and eigenvalues associated with PCA.
EigenFaces is based on Principal Component Analysis
Principal Component Analysis does dimensionality reduction and finds unique features in the training images and removes the similar features from the face images
By getting unique features our recognition task gets simpler
By using PCA you calculate the eigenvectors for your face image data
From these eigenvectors you calculate EigenFace of every training subject or you can say calculating EigenFace for every class in your data
So if you have 9 classes then the number of EigenFaces will be 9
The weight usually means how important something is
In EigenFaces weight of a particular EigenFace is a vector which just tells you how important that particular EigenFace is in contributing the MeanFace
Now if you have 9 EigenFaces then for every EigenFace you will get exactly one Weight vector which will be of N dimension where N is number of eigenvectors
So every element out N elements in one weight vector will tell you how important that particular eigenvector is for that corresponding EigenFace
The facial Recognition in EigenFaces is done by comparing the weights of training images and testing images with some kind of distance function
You can refer this github link: https://github.com/jayshah19949596/Computer-Vision-Course-Assignments/blob/master/EigenFaces/EigenFaces.ipynb
The code on the above link is a good documented code so If you know the basics you will understand the code
I have a dataset consisting of fMRI images (from mice) which are divided into 4 groups (different drug dose levels applied). Each fMRI image is 4D, that means each voxel is a time series. For each fMRI image I want to extract one feature vector.
Now I want to use wavelet decomposition for feature extraction. In Matlab there exist no 4D wavelet decomposition, so I turn the 4D images into 3D by taking the average of the time series. Then I could apply 3D wavelet decomposition and taking the LL component as features, that means doing something like that:
WT = wavedec3(fMRI, 4, 'db4');
LL = WT.dec(1);
temp = cell2mat(LL);
feature_vector = temp(:);
Of course afterwards feature selection algorithms (like recursive feature elimination) could be applied to reduce dimensionality.
What do you think of this approach? Are there better approaches?
My problem is as follows:
I have 6 types of images, or 6 classes. For example, cat, dog, bird, etc.
For every type of image, I have many variations of that image. For example, brown cat, black dog, etc.
I'm currently using a Support Vector Machine (SVM) to classify the images using one-versus-rest classification. I'm unfolding each image into a single pixel vector and using that as the feature vector for a given image I'm experiencing decent classification accuracy, but I want to try something different.
I want to use image descriptors, particularly SURF features, as the feature vector for each image. This issue is, I can only have a single feature vector per given image and I'm given a variable number of SURF features from the feature extraction process. For example, 1 picture of a cat may give me 40 SURF features, while 1 picture of a dog will give me 68 SURF features. I could pick the n strongest features, but I have no way of guaranteeing that the chosen SURF features are ones that describe my image (for example, it could focus on the background). There's also no guarantee that ANY SURF features are found.
So, my problem is, how can I get many observations (each being a SURF feature vector), and "fold" these observations into a single feature vector which describes the raw image and can fed to an SVM for training?
Thanks for your help!
Typically the SURF descriptors are quantized using a K-means dictionary and aggregated into one l1-normalized histogram. So your inputs to the SVM algorithm are now fixed in size.
In OpenCV, there is a CvSVM class which takes in a matrix of samples to train the SVM. The matrix is 2D, with the samples in the rows.
I created my own method to generate a histogram of oriented gradients (HOG) off of a video feed. To do this, I created a 9 channeled matrix to store the HOG, where each channel corresponds to an orientation bin. So in the end I have a 40x30 matrix of type CV_32FC(9).
Also made a visualisation for the HOG and it's working.
I don't see how I'm supposed to feed this matrix into the OpenCV SVM, because if I flatten it, I don't see how the SVM is supposed to learn a 9D hyperplane from 1D input data.
The SVM always takes in a single row of data per feature vector. The dimensionality of the feature vector is thus the length of the row. If you're dealing with 2D data, then there are 2 items per feature vector. Example of 2D data is on this webpage:
http://www.csie.ntu.edu.tw/~cjlin/libsvm/
code of an equivalent demo in OpenCV http://sites.google.com/site/btabibian/labbook/svmusingopencv
The point is that even though you're thinking of the histogram as 2D with 9-bin cells, the feature vector is in fact the flattened version of this. So it's correct to flatten it out into a long feature vector. The result for me was a feature vector of length 2304 (16x16x9) and I get 100% prediction accuracy on a small test set (i.e. it's probably slightly less than 100% but it's working exceptionally well).
The reason this works is that the SVM is working on a system of weights per item of the feature vector. So it doesn't have anything to do with the problem's dimension, the hyperplane is always in the same dimension as the feature vector. Another way of looking at it is to forget about the hyperplane and just view it as a bunch of weights for each item in the feature vector. In this case, it needs one weighting for every item, then it multiplies each item by its weighting and outputs the result.