Questions tagged [features]
A feature is a property which can be used to describe objects in machine learning. Most of the time those are values in R^n, but not always.
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Interpretability of feature weights from Gaussian process classifier [closed]
Suppose I trained a Gaussian process classifier with a linear kernel (using GPML toolbox) and got some feature weights for each input feature.
My question is then:
Does it/When does it make sense ...
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1answer
74 views
Why is it not always possible to compute the centroid of feature vectors?
Hi in the data mining and machine learning course that I'm taking there is a subject on feature spaces and there is this part about feature vector aggregation and metric spaces that I don't really ...
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Descriptors in key-features during mapping
This question is related to this question.
In SLAM-type applications the feature detectors like SIFT are used for finding keypoints in the images to be matched. The candidate keypoints are matched by ...
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1answer
65 views
Terminology: Difference between decision variables, features and attributes?
Could there be a difference between the words "feature", "attribute", and "decision variable" when used in the same paper? The one I am specifically thinking about is about an optimization method for ...
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2answers
76 views
What is the official name of a specific type of combination algorithm
Say that I have the following set of variables:
[A,B,C,D,E,F,G,H,I,J,K,L,M,N,O,P,Q,R,S,T,U,V,W,X,Y,Z]
The values represent a list of variables from a dataset. ...
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1answer
39 views
Problem with Understanding “correlated attributes into a set of values of uncorrelated attributes” in PCA
I am studying PCA. I have a problem in understanding the following concept:
What is meant by transforming "correlated attributes into a set of values of uncorrelated attributes" in Principal ...
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2answers
204 views
Why do AlphaGo and AlphaGo Zero include board history in the input features
Both AlphaGo and AlphaGo Zero include prior board states as input features (the "Turns Since" planes for AlphaGo, and the repeated 8-step history planes for AlphaGo Zero).
What is the purpose of ...
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How to find the matched SIFT features that are spatially consistent?
I have extracted DenseSIFT from the query and database image and quantized by kmeans. The challenge is to find those SIFT features that quantized to the same visual ...
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1answer
53 views
Computer vision methods without “pre-training”
I'm new to computer vision and I have a common question that I couldn't figure out with Internet or books.
As I understood, in general, there are two main approaches in modern computer vision: neural ...
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0answers
30 views
Name of technique of counting pixels above geographic center line
I heard about a technique today which is sometimes used in the classification of 2D images of handwritten glyphs. It goes like this:
Find the highest activated pixel of the image, the lowest ...
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1answer
34 views
Feature selections : Still a high dimentionality
One of the advantage of using a features reduction/sub feature selection is to avoid a high dimentionality.
The most known method is the Forward selection, where it finds the best combinations of ...
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0answers
51 views
Question about (in)dependent Haar-features and (Ada)boosting
I have an old exam question for my pattern recognition course which is stated as follows:
Consider a pool of Haar features, determine if these Haar features are independent or not. Is it a problem ...
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3answers
396 views
Computation of normalized first derivative in discrete case
I am reading a paper on recognition of online handwritten characters. One of the features proposed in the paper is "normalized first derivatives, $(\hat{x}'_t,\hat{y}'_t)$", which they have defined as ...
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1answer
91 views
Description of shape in a vector form
I would like to ask for references to algorithms that can project shape information about an object to 1 dimension. Specifically I am training a neural network to be able to identify objects with ...
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0answers
107 views
More Efficient Feature Method Than Haar-Feature For Face Detection
The seminal work by Viola-Jones had the most impact in face detection. It used Haar-Feature, which is fast to compute with the help of integral image method.
Is there a feature method that is more ...
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0answers
53 views
How the SIFT results vary by changing some parameters
I'm studying the SIFT algorithm and I have some problem understanding its operation, in particular I don't understand how the results vary according to the parameters: number of octaves, number of ...
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0answers
498 views
Why are SIFT features significantly different after simply re-sizing image?
I'm new to SIFT and was surprised by how much the features changed after resizing an image. Here's the images I used for testing:
Original:
Resized:
Cropped:
After running SIFT on those 3 images, ...
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1answer
230 views
How to detect Farkas or MPEG4 FDP points on image with a face?
Brief problem description
I'm using a Basel Morphable Face Model, which was labeled with MPEG4 and Farkas landmarks. I can generate different faces, use different lighting conditions, rotations, ...
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1answer
222 views
How does an image classifier select relevant features?
I am trying to understand cascade classifiers for computer vision. I use OpenCV Traincascade for now and successfully trained some cascades.
However I am trying to grasp the whole process. One thing ...
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1answer
97 views
Use of Activation Units in Facial Expression Understanding
The topic of extracting the Facial Action Coding System (FACS) Action Units (AUs) [1] from images and it's translation into emotion prediction [2] is pretty well studied, but I'm not clear on how it ...
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What are the best features to identify heads from any angle?
I want to automatically track heads in CCTV records.
Sometimes I need to re-identify heads because of multiple heads crossing each other. So I continually need to extract features from tracked heads ...
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1answer
86 views
Classification training data, but regression prediction
Suppose I'm performing machine learning on a simple dataset, and have a bunch of training data of the form:
...
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0answers
1k views
Classifying responses into yes/no
So my problem is as follows: I get responses (such as "yeah whatever", "yes do it", "no don't do it", "nah", "yeah do it" etc.) and I need to classify them into either "yes" or "no" i.e. a binary ...
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1answer
129 views
characteristic vectors for systems
The question is motivated from a physics problem:
Let's first discuss the 1D infinitely long discrete system on a lattice, a system can look like:
system 1:
...(ABAC)(ABAC)(ABAC)...
this leads to ...
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1answer
374 views
Convolutional Neural Network Feature Engineering?
I'm working through the tensorflow tutorial, and I see how you go from 28 x 28 to zero-padding and applying a 5x5x32 convolution to get 28x28x32 and max-pooling etc. What I'm confused about is the 32 ...
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1answer
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What is the difference between 'features' and 'descriptors' in computer vision / machine learning?
I've read multiple time sentences similar to
Finally, for standard image classification bag-of-words
features based on SIFT descriptors have been found
critical for high performances. We first ...
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1answer
2k views
How are HOGs calculated?
Histograms of oriented gradients for human detection seems to be the paper from which all other papers cite when they use HOG features. However, this was the only description I could find in it:
[.....
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1answer
154 views
Featurizing images of different dimensions
I'm building a non linear svm for images to solve a classification problem with domain {0, 1} and I'm currently doing featurization. What I want to do is create 3 features for each pixel representing ...
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1answer
801 views
How to use frame based speech features for learning using a neural network classifier?
I am doing supervised learning on speech audio files using neural networks. For this purpose, I'll have to extract features from the audio file. But since an audio file is a time varying signal, it is ...
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1answer
183 views
Feature values range
Suppose I am about to use SVM for learning a classification or ranking function. Suppose that my feature vectors are two dimensional and that values for one dimension are, say, natural numbers and the ...
