A. Alvarez-Gila, “Scene Recognition for Improved Aesthetic Quality Inference of Photographic Images,” Master’s thesis, Gøvik University College, Gøvik, Norway, Jun. 2010.
Abstract:
The novel field of aesthetic quality inferencing of natural images deals with the automatic assessment of the aesthetic value of a given photograph, by either numerically rating it, or by classifying it as a professional, high-level picture or as a low quality snapshot. Based on the extraction of low-level features, a number of authors have tried to bridge the aesthetic gap given by the inherently subjective nature of aesthetics and, using machine learning techniques, set the basis for the development of a field with potential applications in areas as diverse as 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. Our approach aims to go one step further in the understanding of the image content and, under the assumption that different subject categories require different composition techniques, introduces an additional scene type classification step, which, combined with the use of state of the art feature sets, should yield a significant improvement over the current performance results.
Bibtex:
@phdthesis{aitoralvarez-gila_scene_2010,
address = {G{\o}vik, Norway},
type = {Master's Thesis},
title = {Scene for of },
abstract = {The novel field of aesthetic quality inferencing of natural images deals with the automatic assessment of the aesthetic value of a given photograph, by either numerically rating it, or by classifying it as a professional, high-level picture or as a low quality snapshot. Based on the extraction of low-level features, a number of authors have tried to bridge the aesthetic gap given by the inherently subjective nature of aesthetics and, using machine learning techniques, set the basis for the development of a field with potential applications in areas as diverse as 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.
Our approach aims to go one step further in the understanding of the image content and, under the assumption that different subject categories require different composition techniques, introduces an additional scene type classification step, which, combined with the use of state of the art feature sets, should yield a significant improvement over the current performance results.},
language = {en},
school = {G{\o}vik University College},
author = ,
month = jul,
year = {2010},
}