feature-extraction

问题 I am working on a project where I am have image files that have been malformed (fuzzed i.e their image data have been altered). These files when rendered on various platforms lead to warning/crash/pass report from the platform. I am trying to build a shield using unsupervised machine learning that will help me identify/classify these images as malicious or not. I have the binary data of these files, but I have no clue of what featureSet/patterns I can identify from this, because visually

问题 Let's say I'm concerned with part of an image that I'm wanting to calculate a GLCM for that's not rectangular. How should I go about this? I've made a masking procedure that zeroes out the portion of the image that I don't care about, I just don't know how to take this "masked" image without considering the zeroed out portions of the image... Thanks for your help! 回答1: If you are able to assign the zero intensity value to background pixels, you can obtain the GLCM of the region of interest by

问题 I want to implement FCFS on my training set to select the most relevant and unredundant dependent variables using the r package (Biocomb) then test the selected feature on svm to ensure their efficiency on the classifier performance(svm) but i didn't understand the parameter attrs.nominal Do i have to add all nominal variables in my dataset in order to discretize them ? Any explanation will be appreciated. Error in [.data.frame (data.validation, , 72) : undefined columns selected Error during

问题 I am trying to extract some features from an audio sample using OpenSMILE, but I'm realizing how difficult it is to set up a config file. The documentation is not very helpful. The best I could do was run some of the sample config files that are provided, see what came out, and then go into the config file and try to determine where the feature was specified. Here's what I did: I used the default feature set used from The INTERSPEECH 2010 Paralinguistic Challenge (IS10_paraling.conf). I ran

问题 I am attempting to implement the OpenCV 3.0.0 SURF Feature Description and Detection but after running the sample code on the OpenCV site, I receive a load of errors all related to SURF. Any idea of what could be going wrong? Thanks! #include <stdio.h> #include <iostream> #include "opencv2/core.hpp" #include "opencv2/features2d.hpp" #include "opencv2/highgui.hpp" #include "opencv2/calib3d.hpp" #include "opencv2/xfeatures2d.hpp" #include <opencv2/nonfree/nonfree.hpp> using namespace cv; using

问题 The binning process, which is part of the point feature histogram estimation, results in b^3 bins if only the three angular features (alpha, phi, theta) are used, where b is the number of bins. Why is it b^3 and not b * 3 ? Let's say we consider alpha. The feature value range is subdivided into b intervals. You iterate over all neighbors of the query point and count the amount of alpha values which lie in one interval. So you have b bins for alpha. When you repeat this for the other two

问题 I have a series of 2d images of two types, either a star or a pentagon. My aim is to classify all of these images respectively. I have 30 star images and 30 pentagon images. An example of each image is shown side by side here: Before I apply the KNN classification algorithm, I need to extract a feature vector from all the images. The feature vectors must all be of the same size however the 2d images all vary in size. I have extracted read in my image and I get back a 2d array with zeros and

问题 I'm trying to calculate some textural measures using the GLCM described by Haralick (energy, homogeneity, etc.) for a series of 4 band (R, G, B, NIR) aerial photographs that I have. I have tried this on a subset but am ending up with an image that is mostly blank. My current understanding is that it has to do with the greyscaling and the levels parameter but I can't figure it out. My date is very large (several GB) so I'm trying to be efficient by using the module RIOS (reads an image in as a

问题 In Tensorflow there is already a function to create feature by crossing columns tf.feature_column.crossed_column , but it is more for category data. How about numeric data? For example, there are 2 columns already age = tf.feature_column.numeric_column("age") education_num = tf.feature_column.numeric_column("education_num") if i want to create a third and fourth feature columns base on age and education_num like this my_feature = age * education_num my_another_feature = age * age How can it

问题 I'm trying to get started working with sift feature extraction using (C++) OpenCv. I need to extract features using SIFT, match them between the original image (e.g. a book) and a scene, and after that calculate the camera pose. So far I have found this algorithm using SURF. Does anyone know a base code from which I can get started, or maybe a way to convert the algorithm in the link from SURF to SIFT? Thanks in advance. EDIT: Ok, I worked out a solution for the sift problem. Now I'm trying