Clutter Detection and Removal by Multi-Objective Analysis for Photographic Guidance

TL;DR

This paper presents a camera guidance system that helps photographers identify and remove clutter in photos by estimating object contributions to aesthetics and using GAN-based inpainting for high-quality clutter removal.

Contribution

It introduces a novel multi-objective analysis approach combining aesthetics evaluation and GAN-based inpainting for clutter detection and removal in photography guidance.

Findings

User studies show improved photo quality and efficiency.

The system accurately identifies clutter and suggests effective removal.

High-resolution inpainting maintains image quality after clutter removal.

Abstract

Clutter in photos is a distraction preventing photographers from conveying the intended emotions or stories to the audience. Photography amateurs frequently include clutter in their photos due to unconscious negligence or the lack of experience in creating a decluttered, aesthetically appealing scene for shooting. We are thus motivated to develop a camera guidance system that provides solutions and guidance for clutter identification and removal. We estimate and visualize the contribution of objects to the overall aesthetics and content of a photo, based on which users can interactively identify clutter. Suggestions on getting rid of clutter, as well as a tool that removes cluttered objects computationally, are provided to guide users to deal with different kinds of clutter and improve their photographic work. Two technical novelties underpin interactions in our system: a clutter distinguishment algorithm with aesthetics evaluations for objects and an iterative image inpainting algorithm based on generative adversarial nets that reconstructs missing regions of removed objects for high-resolution images. User studies demonstrate that our system provides flexible interfaces and accurate algorithms that allow users to better identify distractions and take higher quality images within less time.