2,292 research outputs found
Pandora: Description of a Painting Database for Art Movement Recognition with Baselines and Perspectives
To facilitate computer analysis of visual art, in the form of paintings, we
introduce Pandora (Paintings Dataset for Recognizing the Art movement)
database, a collection of digitized paintings labelled with respect to the
artistic movement. Noting that the set of databases available as benchmarks for
evaluation is highly reduced and most existing ones are limited in variability
and number of images, we propose a novel large scale dataset of digital
paintings. The database consists of more than 7700 images from 12 art
movements. Each genre is illustrated by a number of images varying from 250 to
nearly 1000. We investigate how local and global features and classification
systems are able to recognize the art movement. Our experimental results
suggest that accurate recognition is achievable by a combination of various
categories.To facilitate computer analysis of visual art, in the form of
paintings, we introduce Pandora (Paintings Dataset for Recognizing the Art
movement) database, a collection of digitized paintings labelled with respect
to the artistic movement. Noting that the set of databases available as
benchmarks for evaluation is highly reduced and most existing ones are limited
in variability and number of images, we propose a novel large scale dataset of
digital paintings. The database consists of more than 7700 images from 12 art
movements. Each genre is illustrated by a number of images varying from 250 to
nearly 1000. We investigate how local and global features and classification
systems are able to recognize the art movement. Our experimental results
suggest that accurate recognition is achievable by a combination of various
categories.Comment: 11 pages, 1 figure, 6 table
History of art paintings through the lens of entropy and complexity
Art is the ultimate expression of human creativity that is deeply influenced
by the philosophy and culture of the corresponding historical epoch. The
quantitative analysis of art is therefore essential for better understanding
human cultural evolution. Here we present a large-scale quantitative analysis
of almost 140 thousand paintings, spanning nearly a millennium of art history.
Based on the local spatial patterns in the images of these paintings, we
estimate the permutation entropy and the statistical complexity of each
painting. These measures map the degree of visual order of artworks into a
scale of order-disorder and simplicity-complexity that locally reflects
qualitative categories proposed by art historians. The dynamical behavior of
these measures reveals a clear temporal evolution of art, marked by transitions
that agree with the main historical periods of art. Our research shows that
different artistic styles have a distinct average degree of entropy and
complexity, thus allowing a hierarchical organization and clustering of styles
according to these metrics. We have further verified that the identified groups
correspond well with the textual content used to qualitatively describe the
styles, and that the employed complexity-entropy measures can be used for an
effective classification of artworks.Comment: 10 two-column pages, 5 figures; accepted for publication in PNAS
[supplementary information available at
http://www.pnas.org/highwire/filestream/824089/field_highwire_adjunct_files/0/pnas.1800083115.sapp.pdf
On Multifractal Structure in Non-Representational Art
Multifractal analysis techniques are applied to patterns in several abstract
expressionist artworks, paintined by various artists. The analysis is carried
out on two distinct types of structures: the physical patterns formed by a
specific color (``blobs''), as well as patterns formed by the luminance
gradient between adjacent colors (``edges''). It is found that the analysis
method applied to ``blobs'' cannot distinguish between artists of the same
movement, yielding a multifractal spectrum of dimensions between about 1.5-1.8.
The method can distinguish between different types of images, however, as
demonstrated by studying a radically different type of art. The data suggests
that the ``edge'' method can distinguish between artists in the same movement,
and is proposed to represent a toy model of visual discrimination. A ``fractal
reconstruction'' analysis technique is also applied to the images, in order to
determine whether or not a specific signature can be extracted which might
serve as a type of fingerprint for the movement. However, these results are
vague and no direct conclusions may be drawn.Comment: 53 pp LaTeX, 10 figures (ps/eps
Computer Analysis of Architecture Using Automatic Image Understanding
In the past few years, computer vision and pattern recognition systems have
been becoming increasingly more powerful, expanding the range of automatic
tasks enabled by machine vision. Here we show that computer analysis of
building images can perform quantitative analysis of architecture, and quantify
similarities between city architectural styles in a quantitative fashion.
Images of buildings from 18 cities and three countries were acquired using
Google StreetView, and were used to train a machine vision system to
automatically identify the location of the imaged building based on the image
visual content. Experimental results show that the automatic computer analysis
can automatically identify the geographical location of the StreetView image.
More importantly, the algorithm was able to group the cities and countries and
provide a phylogeny of the similarities between architectural styles as
captured by StreetView images. These results demonstrate that computer vision
and pattern recognition algorithms can perform the complex cognitive task of
analyzing images of buildings, and can be used to measure and quantify visual
similarities and differences between different styles of architectures. This
experiment provides a new paradigm for studying architecture, based on a
quantitative approach that can enhance the traditional manual observation and
analysis. The source code used for the analysis is open and publicly available
- …