A. Alvarez-Gila, “Aesthetic Quality Inference of Photographic Images - Survey and Future Trends,” Colour Research for European Advanced Technology Employment Conference (CREATE), Norway, Jun. 2010.
Abstract:
In this paper, we perform a state of the art analysis of the field of the inference of the aesthetic quality of photographic images. The aim of this novel research area is to automatically assess the aesthetic value of a given photograph, by either numerically rating it or classifying it as a professional, high level picture or as a low quality average snapshot. Based on the extraction of low level features from the images, a number of authors have tried to bridge the aesthetic gap given by the inherently subjective nature of aesthetics and, making use of machine learning techniques, set the basis for the development of a field with potential applications in areas as diverse as Content-Based Indexing and Retrieval (CBIR), management and editorial work or consumer photography. Their methods range from the opaque, black-box approach to more content-aware procedures, which define their feature set after well-established photographic techniques and build their success upon the prior identification of the photographic subject. Future trends mentioned here show a promising way ahead.
Bibtex:
@inproceedings{alvarez-gila_aesthetic_2010,
address = {Gj{\o}vik University College, Gj{\o}vik, Norway},
title = {Aesthetic of : and },
isbn = {ISBN: 978-82-91313-46-7},
abstract = {In this paper, we perform a state of the art analysis of the field of the inference of the aesthetic quality of photographic images. The aim of this novel research area is to automatically assess the aesthetic value of a given photograph, by either numerically rating it or classifying it as a professional, high level picture or as a low quality average snapshot. Based on the extraction of low level features from the images, a number of authors have tried to bridge the aesthetic gap given by the inherently subjective nature of aesthetics and, making use of machine learning techniques, set the basis for the development of a field with potential applications in areas as diverse as Content-Based Indexing and Retrieval (CBIR), management and editorial work or consumer photography. Their methods range from the opaque, black-box approach to more content-aware procedures, which define their feature set after well-established photographic techniques and build their success upon the prior identification of the photographic subject. Future trends mentioned here show a promising way ahead.},
language = {en},
booktitle = {The CREATE (Colour Research for European Advanced Technology Employment) 2010 - `'},
author = ,
month = jun,
year = {2010},
pages = {1-5}
}