Questions tagged [feature-extraction]

For questions related to the concept of feature extraction, which is a set of techniques used to derive or create features from the existing set of features. Feature extraction is different from feature selection, which is used to select a subset of the existing features.

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Deep learning feature extraction using image processing batch script?

I am planning a CNN deep learning project using photos of handwritten notes, and try to label them. I am still at the early stages, but I expect that accuracy of this neural net would improve, and ...
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Make an NN utilize other NNs as part of its decision process

Suppose I have a NN that learns to predict the time it takes a robot to move between two jobs. That's three inputs (for starters): robot, job A, job B. Not all robots travel at the same speed, and ...
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How to convert color information to 1D feature vector?

We are making a classification model that takes a clip of a movie as an input and predicts who the director is. Roughly speaking, it will be a model that understands film directors' unique style. We ...
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How are the 8 subfigures created in tile coding for offseting uniformly in each dimension the 8 tilings from each other?

The following is a figure from Sutton-Barto (Section Tile Coding, page 219) to show how the choice of offseting uniformly in each dimension the tilings from each other affects generalization. It is ...
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Table lookup as a special case of linear value function approximation: Could someone give a simple example for $x^{table}(S)$?

In David Silver's lecture notes (Lecture 6, Slide 15), we have: Could someone give a simple example for a table lookup and $x^{table}(S)$ for a selected state $S$?
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A technique of aggregating many input images to a single representation of the relevant features within

I have a few thousand images and I would like to generate a representation of the foreground patterns within - a composition of all of its features, so to speak. In simple terms: take 10000 images of ...
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Is the phrase "Feature Pyramid Network" refer to CNN only?

"Feature Pyramid Network" is a network that is used for feature extraction. Since it is pyramid in shape, it might be called so. Consider the following excerpts from two different sources #1 ...
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Derive information for sub-scoring from one scoring model

I am currently working in Python with a random forest algorithm to perform a scoring. My output is binary. The idea now is to derive sub-scores from the above model that give an opinion on different ...
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Bag of Tricks: n-grams as additional features?

I've been playing with PyTorch's nn.EmbeddingBag for sentence classification for about a month. I've been doing some feature engineering, playing with different ...
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In SIFT, how is the coordinate system being rotated?

I need to understand how SIFT calculates the descriptors for the keypoints. Intuitively, I understand that it takes each keypoint, calculates the gradients for each pixel in a neighborhood of the ...
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How is the bias added after the convolution in a CNN?

I'm having trouble understanding how bias is added to the feature extraction convolution. I've seen people either refer to the bias as a single number that changes per filter or the whole matrix that ...
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2 answers
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What is the difference between feature extraction and fine-tuning in transfer learning?

I'm building a model for facial expression recognition, and I want to use transfer learning. From what I understand, there are different steps to do it. The first is the feature extraction and the ...
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Where do the feature extraction and representation learning differ?

Feature selection is a process of selecting a subset of features that contribute the most. Feature extraction allows getting new features that are not actually present in the given set of features. ...
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Is it a good practice to pad signal before feature extraction?

Is padding, before feature extraction with VGGish, a good practice? Our padding technique is to find the longest signal (which is loaded .wav signal), and then, in ...
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How is a ResNet-50 used for deep feature extraction?

I'm trying to implement the vehicle re-identification model described in https://arxiv.org/pdf/2004.06271.pdf. My question focuses on Section 3.2 of the paper, which uses a ResNet-50 for deep feature ...
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Advice required for identifying bone fragments in CT-scans using STL Files (3D image segmentation)

I am working on a project related to automating the procedure of manually segmenting some bones in CT scans and hopefully if everything goes alright in this stage, move on to do something more with ...
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How can Image Caption work?

I have two models and a file contains captions for images. The output of model 1 is .pkl files that contain the features of the images. Model 2 is the language model that will be trained with the ...
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Can I use the SIFT feature detector on data other than images?

I know how to use SIFT algorithm for images but I never use it for other kinds of data. I have tabular data (x, y, z, time) where x,y,z is the joint position along x, y, z coordinates. Now, can I ...
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When is it necessary to manually extract features to feed into the neural network rather than providing raw data?

Usually, Neural Networks uses raw data. You do not need to extract features manually. NN's can find & extract good features which is a pattern of an image, signal or any kind of data. When we ...
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What is the difference between feature extraction with or without data augmentation?

Here's an extract from Chollet's book "Deep Learning with Python" about using pre-trained CNN to predict class from a photo set (p. 146): At this point, there are two ways you could proceed:...
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How should I use deep learning to find the rotation of an object from its 2D image?

I have 6600 images and I am supposed to know the rotation of the object in each image. So, given an image, I want to regress to a single value. My attempt: I use Resnet-18 to extract a feature vector ...
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2 votes
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Statistical method for selecting features for classification

I'm working on a classifier for the famous MNIST handwritten data set. I want to create a few features on my own, and I want to be able to estimate which feature might perform better before actually ...
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Neural Network for locating shifting resonant frequencies

I have multiple FFT's taken from a sample at different pressures, through different analysis I can see that the resonant frequencies are shifting in the spectrum for each FFT at a different pressure. ...
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Which type of feature extractor do you suggest to classify sensor data?

I have IMU (Inertial Measurment Unit- 6 axis) sensor data. The sensor attached on a car and 7 different drivers wipe on same path. I want to extract features and classify drivers. Which type of ...
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How to deal with Unix timestamps features of sequences, which will be classified with RNNs?

I want to use RNN for classifying whole sequences of events, generated by website visitors. Each event has some categorical properties and a Unix timestamp: ...
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3 votes
2 answers
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Is such a captcha AI-resistant?

Let's say we have a captcha system that consists of a greyscale picture (of a part of a street or something akin to re-captcha), divided into 9 blocks, with 2 missing pieces. You need to choose the ...
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What are some good papers or resources for aspect extraction and opinion modelling from video or audio?

I am quite new to deep learning. I just finished the deep learning specialization by Professor Andrew NG and Deep Learning AI. Now, my professor (instructor) has advised me to look into some classic ...
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2 votes
1 answer
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What are bag-of-features in computer vision?

In computer vision, what are bag-of-features (also known as bag-of-visual-words)? How do they work? What can they be used for? How are they related to the bag-of-words model in NLP?
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Corner detection algorithm gives very high value for slanted edges?

I have tried implementing a basic version of shi-tomasi corner detection algorithm. The algorithm works fine for corners but I came across a strange issue that the algorithm also gives high values for ...
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1 vote
1 answer
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How to choose a suitable threshold value for the Shi-Tomasi corner detection algorithm?

While implementing the Shi-Tomasi corner detection algorithm, I got stuck in deciding a suitable threshold for corner detection. In the Shi-Tomasi algorithm, all those points that qualify $\min( \...
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2 votes
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Visualisation for Features to Predict Timeseries Data

I have a course assignment to use an LSTM to predict the movement directions of stock prices. One of the things I am asked to do is provide a visualization to compare the predictive powers of a set of ...
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Feature extraction for exponentially damped signals

I am looking into exponentially damped signals where it is a stationary signal (after implementing the Adfuller statistical test) and I would like to look into how can I extract meaningful features ...
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What are examples of commonly used feature and readout maps?

It is well-known that deep feedforward networks can approximate any continuous function from $\mathbb{R}^k$ to $\mathbb{R}^l$, (uniformly on compacts). However, in practice feature maps are ...
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Extract product information from email receipt HTML [closed]

I am trying to extract product information from email receipts HTML. Most services I have found focus on OCR from paper receipts or PDFs. I would imagine that extraction of product information would ...
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8 votes
2 answers
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Is word embedding a form of feature extraction?

Feature extraction is a concept concerning the translation of raw data into the inputs that a particular machine learning algorithm requires. These derived features from the raw data that are actually ...
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Plot class activation heatmap of Caffe Model in Python

Given the following 3 research papers, the authors have shown different heatmap graphical representations for features of the trained CNN models: On the performance of Convnet feature for place ...
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2 votes
1 answer
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What are the features get from a feature extraction using a CNN?

I've just started to learn CNN and somewhere I have read if I remove the last FCL I will get the features extracted from the input image but... what are those features? Are they numbers? Labels? An ...
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1 vote
1 answer
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How can I use feature extraction in CNN with image segmentation?

I'm just started to learn about meta learning and CNN and in most paper that I've read they mention to have one CNN to feature extraction. These features will help the another network. I don't know ...
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1 vote
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CBIR Evaluation on contextually different data

How good would a CBIR system trained on a dataset, for example, DELF, trained on the Google Landmarks dataset, perform when evaluated on a contextually different dataset such as the WANG or the COREL ...
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2 votes
1 answer
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Extracting Descriptors and feature points for 3d mesh

I'm programming my work with python, and I have a mesh and I want to extract 3d descriptors and feature points from it( trying to work on multi-scale strategy) , to visualize them later on the mesh, ...
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Learning Features from a Pre-trained Network

I am currently working on learning the features provided by a pre-trained network for image retrieval. Currently I take the features provided by the pre-trained network, use global max pooling to ...
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1 vote
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Can we use Autoencoders for unsupervised CNN feature learning?

I searched through the internet but couldn't find a reliable article that answers this question. Can we use Autoencoders for unsupervised CNN feature learning of unlabeled images like the below and ...
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2 votes
1 answer
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Key Point Extraction the best method?

I have been researching about determining some key points on an image, in this case I'm gonna use cloth (top side of human body) pictures. I want to detect some corner points on those. Example: I ...
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2 votes
3 answers
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How do I determine which relevant features have been learned during training in a CNN?

Is there any way to control the extraction of features? How do I determine which features are been learned during training, i.e relevant information is been learned or not?
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6 votes
1 answer
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Are deep learning models suitable for training with sparse data?

I am training a generative adversarial network (GAN) to generate images given edge histogram descriptor (EHD) features of the image. The EHD features are themselves sparse (meaning they contain a lot ...
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