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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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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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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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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20 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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22 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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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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33 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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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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20 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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140 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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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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28 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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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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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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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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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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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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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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22 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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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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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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51 views

Detecting the bounding box of an object in an image?

I have a dataset of images . Each image has an object in it. In a seperate csv file , I have been provided with the coordinates of the bounding box for the object in the training images. How do I ...
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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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858 views

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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40 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
111 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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1answer
152 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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11 views

Does it make sense to talk about over/under segmentation in a binary segmentation problem?

I was recently discussing with a college about thresholding a probabilistic map such that if the threshold is too low more pixels become part of the segmented area, and if it's to high there will be ...
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25 views

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 ...
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Metric for evaluating predicted bounding boxes from semantic segmentation on an object level outside of training

Context For simplicity let us pretend we are performing semantic segmentation on a series of one pixel high images of width w with three channels (r, g, b) with n label classes. In other words, a ...
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Imposing independence constraints in mixture modeling of correlated data?

For 1-D signals (spectra) or 2-D signals (images), is there a way to impose the constraint that the data within a group is uncorrelated? I am iteratively applying background correction model fitted to ...
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Spreadsheet segmentation

I work on an spreadsheet segmentation/ml-based-parsing project. Input spreadsheets vary in shape and formatting to some extend. Goal is to transform any given spreadsheet into normalized database ...
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188 views

Why is it possible to train a semantic segmentation neural network like U-net/Tiramisu from scratch using small data-set like few hundreds

Why is it possible to train a semantic segmentation neural network like U-net/Tiramisu from scratch using small dataset like few hundreds. While for the image classification task, it is not ...
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508 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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Personalize Recommendations for small dataset

I'm working on a recommender system for a set of niche products. These are products that don't have a large number of customers. Does anyone have any tips on algorithms or approaches that work well ...
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29 views

biological fluorescence supervised machine learning [closed]

Some general questions... So it appears with most classification learners one must come up with a series of quantifiable variables to associate with each observation. This might include mean and std ...
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79 views

How to make 65 clusters ? Is k-mean good algorithm to do this?

I am trying to segment customers based on demographic, behavioral, lifestyle etc into 60-65 segments inline with Claritas Prizm segments Link1 Link2 I have 1 million records and 264 variables. ...
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29 views

Feature Identification for customer profiling

Problem: I want to identify the characteristic(s) of people who would shop on Monday vs Sunday (or any such dichotomous response variable). I have over a million observations and >50 variables/...
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R - How to recover breakpoints from a “breakpoints” object [closed]

I have used the "breakpoints" function from within the "strucchange" package to find the optimum number of breaks and their points in time ready for the linear segmentation of a time series. I know ...
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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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1answer
1k views

How to implement a weight map for a U-Net [closed]

I have a question regarding the convolutional neural network known as U-Net (see link below) and hope somebody can help me out. https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/ In the ...
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How can we model the visit pattern of a customer in a retail store?

I am trying to segment customers based on how recently they visited the store. However, I am also trying to incorporate the frequency with which they visit the store. For example, in a data collected ...
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1answer
2k views

how can my loss suddenly increase while training a CNN for image segmentation?

I work with keras 1.2.2 with a tensorflow 1.4.0 backend. I'm using a unet architecture, I have 708 images of 650x650 pixels and 6 chanels. I augmented the dataset with mirrorings and rotations, for a ...
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92 views

Hierarchical clustering on dependent data

I do customer clustering based on weekly data. I don't want to rely on only one week of data so I was considering pooling the data over a few weeks together and assume that these are simply "different"...
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322 views

Customer Segmentation Using RFM Interpretation of Negative Scaled Variables

I am doing customer segmentation using RFM. Prior to proceeding with the clustering, I log transformed my Monetary variable to account for skewness. ...
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66 views

Quantify whether a set of binary segmentation models (experts) have diversity on a fixed dataset?

I have a set of models for binary segmentation task (M=10), and a set of images (N=1000). I also can collect the prediction map for all these N=1000 images, resulting in 10,000 grayscale maps. Now, ...