Abstract
The fuzzy similarity analysis we propose in this paper is used for unsupervised image classification. We introduce a special growing unsupervised learning algorithm for information compression (granulation) of the original “raw data” (the RGB pixels) of an image with a smaller number of neurons (information granules). Two important parameters are extracted from each image, namely the center of gravity (COG) and the model volume of the image, taken as the number of neurons obtained from information compression. These two features are used as inputs for special fuzzy inference for numerically calculating the degree of similarity between a pair of images. The fuzzy inference procedure can be tuned based on a predefined human preference (list of similar images), thus performing human-assisted similarity analysis. The choice of the optimization algorithm and the selection of the optimization criterion are questions open to the user to answer. The proposed computation scheme for similarity analysis is illustrated on a test example of 16 flower images and results are discussed.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Advanced Computational Intelligence and Intelligent Informatics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
