Corel Image Features
Donated on 6/30/1999
This dataset contains image features extracted from a Corel image collection. Four sets of features are available based on the color histogram, color histogram layout, color moments, and co-occurence
Dataset Characteristics
Multivariate
Subject Area
Other
Associated Tasks
-
Feature Type
Real
# Instances
68040
# Features
-
Dataset Information
Additional Information
The original image collection was obtained from Corel at http://corel.digitalriver.com/. There are 68,040 photo images from various categories. Each set of features is stored in a separate file. For each file, a line corresponds to a single image. The first value in a line is is the image ID and the subsequent values are the feature vector (e.g. color histogram, etc.) of the image. The same image has the same ID in all files but the image ID is not the same as the image filename.
Has Missing Values?
No
Variables Table
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0 to 10 of 89
Additional Variable Information
From each image four sets of features were extracted: - Color Histogram - Color Histogram Layout - Color Moments - Co-occurrence Texture Color Histogram: 32 dimensions (8 x 4 = H x S) - HSV color space is divided into 32 subspaces (32 colors : 8 ranges of H and 4 ranges of S). - the value in each dimension in a ColorHistogram of an image is the density of each color in the entire image. - Histogram intersection (overlap area between ColorHistograms of two images) can be used to measure the similarity between two images. Color Histogram Layout: 32 dimensions (4 x 2 x 4 = H x S x sub-images) - each image is divided into 4 sub-images (one horizontal split and one vertical split). - 4x2 Color Histogram for each sub-image is computed. - Histogram Intersection can be used to measure the similarity between two images. Color Moments: 9 dimensions (3 x 3) - the 9 values are: (one for each of H,S, and V in HSV color space) -- mean, -- standard deviation, and -- skewness. - Euclidean distance between Color Moments of two images can be used to represent the dis-similarity (distance) between two images. Co-occurrence Texture: 16 dimensions (4 x 4) - images are converted to 16 gray-scale images. - co-ocurrence in 4 directions is computed (horizontal, vertical, and two diagonal directions). the 16 values are: (one for each direction). -- Second Angular Moment, -- Contrast, I -- nverse Difference Moment, and -- Entropy. -Euclidean distance between ColorMoments of two images can be used to measure the dis-similarity (distance) between two images.
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset corel_image_features = fetch_ucirepo(id=119) # data (as pandas dataframes) X = corel_image_features.data.features y = corel_image_features.data.targets # metadata print(corel_image_features.metadata) # variable information print(corel_image_features.variables)
Ortega-Binderberger,Michael. (1999). Corel Image Features. UCI Machine Learning Repository. https://doi.org/10.24432/C5K599.
@misc{misc_corel_image_features_119,
author = {Ortega-Binderberger,Michael},
title = {{Corel Image Features}},
year = {1999},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5K599}
}Creators
Michael Ortega-Binderberger
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.