Context-aware Clustering and Assessment of Photo Collections

Abstract
To ensure that all important moments of an event are represented and that challenging scenes are correctly captured, both amateur and professional photographers often opt for taking large quantities of photographs. As such, they are faced with the tedious task of organizing large collections and selecting the best images among similar variants. Automatic methods assisting with this task are based on independent assessment approaches, evaluating each image apart from other images in the collection. However, the overall quality of photo collections can largely vary due to user skills and other factors. In this work, we explore the possibility of contextaware image quality assessment, where the photo context is defined using a clustering approach, and statistics of both the extracted context and the entire photo collection are used to guide identification of low-quality photos. We demonstrate that our method is able to exibly adapt to the nature of processed albums and to facilitate the task of image selection in diverse scenarios.
Description

        
@inproceedings{
10.1145:3092912.3092916
, booktitle = {
Computational Aesthetics
}, editor = {
Holger Winnemoeller and Lyn Bartram
}, title = {{
Context-aware Clustering and Assessment of Photo Collections
}}, author = {
Kuzovkin, Dmitry
and
Pouli, Tania
and
Cozot, Rémi
and
Meur, Olivier Le
and
Kervec, Jonathan
and
Bouatouch, Kadi
}, year = {
2017
}, publisher = {
Association for Computing Machinery, Inc (ACM)
}, ISSN = {
1816-0859
}, ISBN = {
978-1-4503-5080-8
}, DOI = {
10.1145/3092912.3092916
} }
Citation