Human Image Complexity Analysis Using a Fuzzy Inference System

Image complexity has been estimated by computational algorithms that try to simulate the human criterion to determine complexity. Image complexity has been used to study the behavior of the human brain, as well as in the area of image processing. This paper presents a work to analyze image complexit...

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Bibliographic Details
Main Authors: Madrid-Herrera, Luis, Chacon-Murguia, Mario I., Posada-Urrutia, Daniel A., Ramirez-Quintana, Juan A.
Format: Conference Proceeding
Language:English
Subjects:
Complexity theory
fuzzy inference
Fuzzy logic
Fuzzy systems
human criterion
Image color analysis
image complexity
Principal component analysis
Training
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Summary:Image complexity has been estimated by computational algorithms that try to simulate the human criterion to determine complexity. Image complexity has been used to study the behavior of the human brain, as well as in the area of image processing. This paper presents a work to analyze image complexity. A fuzzy inference system using principal component analysis, PCA, is designed to model the human criterion. The PCA characteristics are obtained from the most used features reported in the literature; contrast, correlation, energy, homogeneity, frequency factor, edge density, compression ratio, number of regions, colorfulness, number of colors, and color harmony. The work was achieved with the data based RS1 and RS2. The results obtained by cross validation, demonstrate a correlation level of 0.8566 between the proposed method and the human criterion.
ISSN:1558-4739
DOI:10.1109/FUZZ-IEEE.2019.8858966