Defining a Metacolor Space Representation to Perform Image Segmentation

Ciro Castiello; Nicoletta Del Buono; Flavia Esposito

doi:10.20944/preprints202402.0140.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 2 February 2024 / Approved: 2 February 2024 / Online: 2 February 2024 (09:29:20 CET)

In this paper, we use a machine learning method, known as Nonnegative Matrix Factorization, to extract from a given image some new information obtained from a number of color space representations. Such kind of information, termed ``metacolor'', can be considered as a particular color space representation of the image, which can be used to obtain a binary segmentation of the investigated image by distinguishing foreground and background pixels. Some numerical experiments prove that the proposed method shows improved results when compared with common simpler thresholding algorithms.

Color Spaces; Low Rank Data Representation; Feature Extraction; Machine Learning algorithm; Nonnegative Matrix Facorization; Image Segmentation

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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Defining a Metacolor Space Representation to Perform Image Segmentation

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