Questions tagged [segmentation]

In marketing/economics, the task of dividing some population such as customers into sub-groups based on some type of shared characteristics or demands. Statistically, various unsupervised and supervised methods may be used for that (clustering, conjoint analysis, tree, etc.).

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Does anyone have any experience in segmenting motor evoked potentials?

I'm working in EEG and motor imagery and was wondering what the best way to go about sectioning my data points for feature analysis / time synchronizing with events or observable features. Are there ...
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Strategies for time series forecasting for 2000 different products?

First of all, I realise that my question is very broad and that it may be hard to answer this question because of it. Do you have any advice on how to approach a 'problem' where you need to make ...
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Semantic Segmentation Multi-Class Single Channel Output Math

For semantic segmentation problems, I understand that it's a pixel-wise classification problem. At the last layer of the neural network, I would basically have a 1x1x1 convolution layer with a softmax ...
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9 views

Semantic segmentation without background class

For the topic of Few-Shot Learning, I am trying to reuse a segmentation model trained on dataset $D_{1}$ with $classes = \{Background, Person\}$ as a pretrained model for the same training set but ...
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149 views

Clustering Consumer data with over 100 variables and 50000 rows each

I am tasked with performing a clustering exercise for a consumer survey dataset with the hopes of finding distinct consumer segments. In the past, I've done it using a variety of techniques- ...
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16 views

Find interesting segment automatically in funnel analysis

This is a feature that I came across in Mixpanel and really enjoy it, but I haven't figure out how to do it myself (so I can use it on some other dataset). Use case I have a funnel (which I defined ...
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289 views

Customer-Segmentation based on feature importance

The problem: We have asked customer-satisfaction for various shops (variable satisfaction ranging from 1 to 4). In addition we have provided 30 shop features and our participants had to rate if the ...
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About Image Size in image segementation

Objective : A detection aid model for medical doctors to detect brain tumor. sample size : (155, 240, 240) seg size : (155, 240, 240) input shape : (1, 128, 160, 128) output shape : (1, 128, 160, ...
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50 views

Understanding the weighted cross-entropy method of u-net

I am trying to implement the weight-cross entropy mentioned in unet paper to counter the class-imbalances. I am not really able to understand how they are exactly implementing the weight-cross entropy....
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239 views

Multi Label Semantic Segmentation - One class is way behind

I'm trying to build a Multi-Label Semantic Segmentation model, but while training, when I'm looking at the validation set, I can see that one label is far far behind, and in the end, he is not getting ...
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18 views

Experimental data segmentation using trees based on means - could single trial estimates improve reliability?

I have some categorical data and measures of participants accuracy. Let's say that it is a quiz and we have 8 different categories: History, Geography, Physics and so forth. Each participant is ...
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The optimal number of segments for Piecewise Aggregate Approximation (PAA)

I have time series dataset and I want to break it into segments, in order to run autoencoder on individual segments. After some research, PAA does what I need: it breaks the dataset into segments (...
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why encoder/decoder is better than stack of conv layers in segmentation task? [closed]

I have read through several articles that said stacking of conv layers consume lots of computer resources(I guess only run time not memory right?) https://www.jeremyjordan.me/semantic-segmentation/. ...
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If a model can overfit to a minibatch, can I say it can be generalized well with large enough samples?

I am doing a proof-of-concept thing to see if Mask RCNN can do a good instance segmentation on my own dataset. The issue is that I have to annotate the data myself and it takes long time to annotate ...
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1answer
17 views

Using k-means to segment customers in the positive class

I have some labeled data (0=didn’t cancel, 1=canceled) that I am creating a model for in my marketing class. On top of predicting who is likely to cancel, I’d like to explore the possibility of ...
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Data balancing in image classification

I've to segment defects from an image. The image consists of only tomatoes with it's defects in it. The defects and tomatoes in the dataset are as follows: ...
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190 views

Detection of music note sequence in audio signal

I have an audio signal which contains the combination of different western music notes(I know this combination in advance) and I want to identify the sequence of the music notes present in it. For ...
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2k views

Clustering Survey Responses Based on Dichotomous Responses

I have a set of about ten questions that I would like to use to create groupings from. The responses are all dichotomous (responses are in the form of 1 or 2 where 1 and 2 represent differences in ...
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Improving bounding box IoU for document segmentation

I have tried using RCNN and faster RCNN to detect tables and paragraphs. However, I keep encountering the same issue, which I guess is because of region suggestion algorithm; Parts of the first or ...
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101 views

Multivariate change point detection with strongly correlated (nonlinear) variables

I have a two-variable time series. There is a very strong nonlinear correlation between the two variables, so they can be thought of as: variable_1 = $X$ (random variable) variable_2 = $f(X) + N(\mu,...
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230 views

Segmentation of Geodemographic Data

I have a dataset that has 5 variables (columns). These are median house price, median income, number of people with no educational degree, number of people with high school degree, number of people ...
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Is the capacity of a multitask U-Net with two-decoders the same of a standard U-Net with doubled capacity in the decoder?

I implemented a U-Net with an additional decoder (one encoder, then it splits into two decoders). The first decoder predicts the normal segmentation label and the second decoder predicts the distance ...
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Looking for classic image segmentation tool for small RGB images (python)

I am working with 32x32 colour images and want to segment items on them. I am using https://scikit-image.org . This only seems to work on grayscale images and mainly seem to focus on detecting edges ...
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62 views

deciles in skewed distributions [closed]

Does it make sense to use deciles for distribution that are skewed. For example, consider the exponential distribution with lambda >= 1.5 or lambda distribution ...
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35 views

Meaning of sparse annotation for images?

what is sparse annotation? Is it pixel-wise labeling for images. what are the other types of annotation?
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Grouping already known objects from an Image

Assuming my typical training data are images that each contain multiple animals, with the animals themselves already located/segmented (We have information for each pixel whether it is an animal or ...
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258 views

Repeating k-means, is it helpful?

I'm working with k-means algorithm, and I'm proceeding in this way: I've run k-means from 2 to n clusters, I plotted the k-means result of the variance, to get the "elbow", to decide the best trade-...
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216 views

Does Sørensen–Dice Coefficient (Dice Score) only account for true positives?

I'm working in a project on medical image segmentation which uses the Dice Score as part of the loss function, but I got some doubts with the commonly adopted implementation. The definition of Dice ...
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32 views

Krippendorff's Alpha for unitizing texts

I have a question about using alpha to measure Krippendorff's inter-annotator reliability in qualitative data. After reading the papers, I'm still not sure how to handle this case. I'm working on a ...
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25 views

what is the best approach in dealing with large dimension custom data for training and predicting deep learning models

i am trying to implement semantic segmentation for satellite images.My custom dataset has dimensions(height,width)in range (3000, 3000)what is the best approach for feeding(for training) and ...
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893 views

Deep learning models for unsupervised semantic segmentation

I am working on semantic segmentation for satellite images using keras and python. It is my understanding that popular models like U-Net require mask images (labels). Are there any unsupervised deep ...
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Approaches for a “risk segmentation” applied to auto insurance

I wonder about approaches for a risk segmentation applied to auto insurance. i.e. Suppose I have all the states within United States, and I wanna say: "Ok, Alaska and Alabama belong to group 1; ...
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859 views

customer behavior analysis and segmentation using data from loyalty program

I'm trying to do some analysis on customers behavior. Basically, I have information on customer's loyalty points activities data (e.g. how many points they have earned, how many points they have used, ...
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710 views

BatchNorm after ReLU

I am currently experimenting with different settings for a U-Net (https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/) based image segmentation and I was unable to find out if it makes any ...
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30 views

Deciding length of units in sound recognition for training HMMs

I am working on creating a method to detect changes from one song to another. Namely, I hope to use a Hidden Markov Model (HMM) in order to model a part of a song and check to see if it accurately ...
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57 views

Modeling on entire dataset vs. Combining segmentation models trained on subsets of the same dataset

Training machine learning models on an unbalanced dataset: about 3% positive labels, and 97% negative. The modeling goal is to get as many examples as possible with 60% precision on a holdout test set ...
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51 views

How we determine the ground truth box of the object in each frame in Matlab?

When we track one object in a video sequence using a tracking object method, the estimated bounding box is given by the method for every frame of the video. But how we determine the ground truth box ...
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34 views

How to include percentage variables in PCA + K-means when some values are undefined because the denominator is 0?

I'm trying to do customer segmentation by using PCA to reduce dimensionality and then feeding the resulting principal components into a K-means algo to get at the final segments. Some of my variables ...
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23 views

Training neural network with a single instance at a time

For a semantic segmentation problem attempted using neural networks, does it make sense to try achieving overfitting with single training example and then (depending on generalization error) add more ...
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1answer
27 views

How to find categorical contributing factors for an anomaly?

Given a house sales dataset with number of houses sold each day and their attributes (i.e., price, number of rooms, size, etc.) - if on a specific day there's a spike/drop in sales, what are some ...
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434 views

customer segmentation with categorical variables

I have a customer dataset, which is a survey result. I have 1595 obs. and about 200 columns (200 because most of the cases the questions were multiple choice and we had to split it into columns). ...
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How can I isolate objects from a 3D image volume using normally distributed geometric features

I have '.tif' image stacks that I am analyzing as volumes. For every object, I can get the Volume, surface area:volume, 'sphericity' and Euler number. For every one of those features, the objects I am ...
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Multiclass Segmentation Using U-Net: My training loss is not decreasing after certain epoch (accuracy not increasing) [duplicate]

So the problem is to perform a multiclass segmentation (255 classes of crops), and I am using a U-Net model for that. The input images are grayscale and the images of dimensions (128,128,1) are ...
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18 views

How to use Bayesian belief Network map/Causality map for segmentation?

I have obtained the causality map for my data. I have an event of interest and the evidences for the event. How do I make the use of this information to come up with segmentation/clustering such that ...
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26 views

What methods could/should I use for identifying sub-groups in a customer segmentation?

I am working on a segmentation model that has been fed into the company by an external agency which created the segmentation based on surveys. I have a number of 6 attitudinal segments and after ...
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122 views

Why has the Auxiliary loss fallen out of favor?

Why has the Auxiliary loss fallen out of favor? In deep learning, at one point, there were many deep models that utilize multiple softmax loss so that the gradient can flow better at the beginning of ...
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50 views

Future of statistical methods in image segmentation? [closed]

I was looking for a purely statistical method for image segmentation and found many, e.g. Hidden Markov Random Fields with EM algorithm. But it seems to me that these methods are nowadays completely ...
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1answer
266 views

Introduction to Conditional random fields

I came across the application of a conditional random field (CRF) to the output from a convolutional neural network (CNN) for image segmentation. The additional CRF step seems to be a common ...
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3-4-5 Rule How to partition the sets?

In Data Mining course, we are taking 3-4-5 Rule to segments the data uniformly. I'm trying to understand these lines below, and how they are linked to the graph below too. If an interval covers 3, ...
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To segment or not to segment, this is the question

I am starting a project, in which I plan to run a neural-network regression using images. These are simple images of particles in a field with low contrast. The shape of the particles changes in ...