From Self-ception to Image Self-ception: A method to represent an image with its own approximations

TL;DR

This paper introduces 'Image Self-ception,' a novel method that represents images using their own approximations, allowing control over representation accuracy through segmentation, with demonstrated visual results and a supporting video demonstration.

Contribution

It proposes a new self-referential image representation method called 'Image Self-ception' and provides an algorithm to implement it with adjustable accuracy.

Findings

Effective image representations using self-approximation

Demonstrated control over representation accuracy

Visual results and video demonstrations provided

Abstract

A concept of defining images based on its own approximate ones is proposed here, which is called 'Self-ception'. In this regard, an algorithm is proposed to implement the self-ception for images, which we call it 'Image Self-ception' since we use it for images. We can control the accuracy of this self-ception representation by deciding how many segments or regions we want to use for the representation. Some self-ception images are included in the paper. The video versions of the proposed image self-ception algorithm in action are shown in a YouTube channel (find it by Googling image self-ception).