Journal ArticleDOI
Using complexity estimates in aesthetic image classification
TLDR
This work explores the use of complexity estimates to predict the aesthetic merit of photographs, using a set of image metrics and two different classifiers to classify images gathered from a photography web site.Abstract:
In recent years, the search for computational systems that classify images based on aesthetic properties has gained momentum. Such systems have a wide range of potential applications, including image search, organization, acquisition and generation. This work explores the use of complexity estimates to predict the aesthetic merit of photographs. We use a set of image metrics and two different classifiers. Our approach classifies images gathered from a photography web site, attempting to reproduce the evaluation made by a group of users. For this purpose, we use complexity estimate metrics based on the encoding size and compression error of JPEG and fractal compression, which are applied to the original value channel and to the images resulting from applying Sobel and Canny filters to this channel. By employing these estimates, in conjunction with the average and standard deviation of the value channel, i.e., 20 features, a success rate of 74.59% was attained. Using the three most influential features yiel...read more
Citations
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Proceedings ArticleDOI
Image complexity and spatial information
Honghai Yu,Stefan Winkler +1 more
TL;DR: It is found that compression-based complexity of an image normally increases with decreasing resolution, and spatial information (SI) measures strongly correlate with compression- based complexity measures.
Book ChapterDOI
Relationship Between Visual Complexity and Aesthetics: Application to Beauty Prediction of Photos
TL;DR: A set of visual complexity features are proposed and it is shown that the complexity level calculated from the proposed features have a near-monotonic relationship with human beings’ beauty expectation on thousands of photos.
Journal ArticleDOI
Visual complexity modelling based on image features fusion of multiple kernels.
TL;DR: This work uses a dataset composed of 800 visual stimuli divided into five categories, describing each stimulus by 329 features based on edge detection, compression error and Zipf’s law, and concludes that Feature Selection Multiple Kernel Learning obtains the best results.
Journal ArticleDOI
Distinguishing paintings from photographs by complexity estimates
TL;DR: The results of the current study indicate that different estimates related to image complexity achieve better results than state-of-the-art feature sets based on color, texture and perceptual edges.
Journal ArticleDOI
Bandwidth Modeling of Silicon Retinas for Next Generation Visual Sensor Networks
Nabeel Khan,Maria G. Martini +1 more
TL;DR: A two-parameter model is proposed for the dependency of the event rate on scene complexity and sensor speed and achieves a prediction accuracy of approximately 88.4% for the outdoor environment along with the overall prediction performance of approximately 84%.
References
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