Piecewise linear model
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- University of Kansas
- Organisation type
- Academia
- Country
- United States of America
- Published
- 1 November 1973
- Authors
- R. Haralick, K. Shanmugam, I. Dinstein
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 0.4K
- Training data
- 314 tokens
16 input features + bias = 17 input features 7*6/2 = 21 Hyperplanes 17*21 = 357 parameters "For the multicategory problem involving NR categories, a total of NR(NR - 1)/2 hyperplanes are used to partition the pattern space." "The input variables to the classifier consisted of the mean variance of the four textural features (f1,f2,f3, andfg obtained from the distance 1 gray-tone spatial-dependence matrices) and eight spectral features (comprised of the mean variance of the image gray-tone values)…
"Number of training samples = 314;"
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Historical significance,Highly cited
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Textural Features for Image Classification
- Last updated
- 28 November 2025
What the numbers mean
Background
Piecewise linear model was published by University of Kansas, in the country recorded as United States of America, during November 1973. The category the publisher falls under is academia.
It works in the domain of Vision, and is recorded as performing the task of image classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
It was trained on a corpus of about 314 tokens of text.
The reason it appears in this catalogue at all: historical significance,Highly cited.
Answers
Piecewise linear model — common questions
Piecewise linear model— what GPU do I need to run it?
None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Piecewise linear model— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
Piecewise linear model— how many parameters does it have?
It has a parameter count of 0.4K. 16 input features + bias = 17 input features 7*6/2 = 21 Hyperplanes 17*21 = 357 parameters "For the multicategory problem involving NR categories, a total of NR(NR - 1)/2 hyperplanes are used to partition the pattern space." "The input variables to the classifier consisted of the mean variance of the four textural features (f1,f2,f3, andfg obtained from the distance 1 gray-tone spatial-dependence matrices) and eight spectral features (comprised of the mean variance of the image gray-tone values) in each of the four spectral bands". That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Piecewise linear model— who created it?
It was published by University of Kansas, based in United States of America, an organisation categorised as academia.
Piecewise linear model— when was it released?
It was published in November 1973. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Piecewise linear model— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.