feature-extraction

I am using BOW in opencv for clustering the features of variable size. However one thing is not clear from the documentation of the opencv and also i am unable to find the reason for this question: assume: dictionary size = 100. I use surf to compute the features, and each image has variable size descriptors e.g.: 128 x 34, 128 x 63, etc. Now in BOW each of them are clustered and I get a fixed descriptor size of 128 x 100 for a image. I know 100 is the cluster center created using kmeans clustering. But I am confused in that, if image has 128 x 63 descriptors, than how come it clusters into

Does anyone know of a (purely) matlab implementation of Haar feature extraction (the kind used in Viola&Jones object detection algorithm)? (I ran across a matlab binding to Opencv's implementation but this is not what I am looking for) I came across this code published as part of a Google Summer of Code 2007 project under GNU GPLv2 license. Face detection for F-spot Note that I haven't tested it myself, just thought it could be useful for you.. 来源： https://stackoverflow.com/questions/2058138/matlab-implementation-of-haar-feature-extraction

From the TensorFlow docs it's clear how to use tf.feature_column.categorical_column_with_vocabulary_list to create a feature column which takes as input some string and outputs a one-hot vector. For example vocabulary_feature_column = tf.feature_column.categorical_column_with_vocabulary_list( key="vocab_feature", vocabulary_list=["kitchenware", "electronics", "sports"]) Let's say "kitchenware" maps to [1,0,0] and "electronics" maps to [0,1,0] . My question is related to having a list of strings as a feature. For example, if the feature value was ["kitchenware","electronics"] then the desired

I have an image feature extraction problem. The input images are binary (black and white) and may contain blobs of approximately known area and aspect ratio. These need to be fit with ellipses using some best fit algorithm. Example input: Desired output: There may be multiple blobs (zero or more), the number is not known in advance. The approximate area and aspect ratio of all the blobs is known (and is the same). How many are in the image, their position, orientation and actual size are what I'm trying to find. The output should be a best fit ellipse for each blob based on the actual found

I am trying to extract pitch features from an audio file which I would use for a classification problem. I am using python(scipy/numpy) for classification. I think I can get frequency features using scipy.fft but I don't know how to approximate musical notes using frequencies. I researched a bit and found that I need to get chroma features which map frequencies to 12 bins for notes of a chromatic scale. I think there's a chroma toolbox for matlab but I don't think there's anything similiar for python. How should I go forward with this? Could anyone also suggest reading material I should look

I have just started to work on a Classification problem. Its a two class problem, My Trained model(Machine Learning) will have to decide/predict either to allow a URL or Block it. My Question is very specific. How to Classify URLs? Should i use normal text analysis methods? What are URLs Features? How to Select and Extract Features from URL? I assume you do not have access to the content of the URL thus you can only extract features from the url string itself. Otherwise it makes more sense to use the content of the URL. Here are some features I will try. See this paper for more ideas: All url

I have some geographical trajectories sampled to analyze, and I calculated the histogram of data in spatial and temporal dimension, which yielded a time domain based feature for each spatial element. I want to perform a discrete FFT to transform the time domain based feature into frequency domain based feature (which I think maybe more robust), and then do some classification or clustering algorithms. But I'm not sure using what descriptor as frequency domain based feature, since there are amplitude spectrum, power spectrum and phase spectrum of a signal and I've read some references but still