2,358 research outputs found
Context-Aware Embeddings for Automatic Art Analysis
Automatic art analysis aims to classify and retrieve artistic representations
from a collection of images by using computer vision and machine learning
techniques. In this work, we propose to enhance visual representations from
neural networks with contextual artistic information. Whereas visual
representations are able to capture information about the content and the style
of an artwork, our proposed context-aware embeddings additionally encode
relationships between different artistic attributes, such as author, school, or
historical period. We design two different approaches for using context in
automatic art analysis. In the first one, contextual data is obtained through a
multi-task learning model, in which several attributes are trained together to
find visual relationships between elements. In the second approach, context is
obtained through an art-specific knowledge graph, which encodes relationships
between artistic attributes. An exhaustive evaluation of both of our models in
several art analysis problems, such as author identification, type
classification, or cross-modal retrieval, show that performance is improved by
up to 7.3% in art classification and 37.24% in retrieval when context-aware
embeddings are used
Loud and Trendy: Crowdsourcing Impressions of Social Ambiance in Popular Indoor Urban Places
New research cutting across architecture, urban studies, and psychology is
contextualizing the understanding of urban spaces according to the perceptions
of their inhabitants. One fundamental construct that relates place and
experience is ambiance, which is defined as "the mood or feeling associated
with a particular place". We posit that the systematic study of ambiance
dimensions in cities is a new domain for which multimedia research can make
pivotal contributions. We present a study to examine how images collected from
social media can be used for the crowdsourced characterization of indoor
ambiance impressions in popular urban places. We design a crowdsourcing
framework to understand suitability of social images as data source to convey
place ambiance, to examine what type of images are most suitable to describe
ambiance, and to assess how people perceive places socially from the
perspective of ambiance along 13 dimensions. Our study is based on 50,000
Foursquare images collected from 300 popular places across six cities
worldwide. The results show that reliable estimates of ambiance can be obtained
for several of the dimensions. Furthermore, we found that most aggregate
impressions of ambiance are similar across popular places in all studied
cities. We conclude by presenting a multidisciplinary research agenda for
future research in this domain
A Data Set and a Convolutional Model for Iconography Classification in Paintings
Iconography in art is the discipline that studies the visual content of
artworks to determine their motifs and themes andto characterize the way these
are represented. It is a subject of active research for a variety of purposes,
including the interpretation of meaning, the investigation of the origin and
diffusion in time and space of representations, and the study of influences
across artists and art works. With the proliferation of digital archives of art
images, the possibility arises of applying Computer Vision techniques to the
analysis of art images at an unprecedented scale, which may support iconography
research and education. In this paper we introduce a novel paintings data set
for iconography classification and present the quantitativeand qualitative
results of applying a Convolutional Neural Network (CNN) classifier to the
recognition of the iconography of artworks. The proposed classifier achieves
good performances (71.17% Precision, 70.89% Recall, 70.25% F1-Score and 72.73%
Average Precision) in the task of identifying saints in Christian religious
paintings, a task made difficult by the presence of classes with very similar
visual features. Qualitative analysis of the results shows that the CNN focuses
on the traditional iconic motifs that characterize the representation of each
saint and exploits such hints to attain correct identification. The ultimate
goal of our work is to enable the automatic extraction, decomposition, and
comparison of iconography elements to support iconographic studies and
automatic art work annotation.Comment: Published at ACM Journal on Computing and Cultural Heritage (JOCCH)
https://doi.org/10.1145/345888
Visual link retrieval and knowledge discovery in painting datasets
Visual arts have invaluable importance for the cultural, historic and
economic growth of our societies. One of the building blocks of most analysis
in visual arts is to find similarities among paintings of different artists and
painting schools. To help art historians better understand visual arts, the
present paper presents a framework for visual link retrieval and knowledge
discovery in digital painting datasets. The proposed framework is based on a
deep convolutional neural network to perform feature extraction and on a fully
unsupervised nearest neighbor approach to retrieve visual links among digitized
paintings. The fully unsupervised strategy makes attractive the proposed method
especially in those cases where metadata are either scarce or unavailable or
difficult to collect. In addition, the proposed framework includes a graph
analysis that makes it possible to study influences among artists, thus
providing historical knowledge discovery.Comment: submitted to Multimedia Tools and Application
Aesthetic preference for art emerges from a weighted integration over hierarchically structured visual features in the brain
It is an open question whether preferences for visual art can be lawfully predicted from the basic constituent elements of a visual image. Moreover, little is known about how such preferences are actually constructed in the brain. Here we developed and tested a computational framework to gain an understanding of how the human brain constructs aesthetic value. We show that it is possible to explain human preferences for a piece of art based on an analysis of features present in the image. This was achieved by analyzing the visual properties of drawings and photographs by multiple means, ranging from image statistics extracted by computer vision tools, subjective human ratings about attributes, to a deep convolutional neural network. Crucially, it is possible to predict subjective value ratings not only within but also across individuals, speaking to the possibility that much of the variance in human visual preference is shared across individuals. Neuroimaging data revealed that preference computations occur in the brain by means of a graded hierarchical representation of lower and higher level features in the visual system. These features are in turn integrated to compute an overall subjective preference in the parietal and prefrontal cortex. Our findings suggest that rather than being idiosyncratic, human preferences for art can be explained at least in part as a product of a systematic neural integration over underlying visual features of an image. This work not only advances our understanding of the brain-wide computations underlying value construction but also brings new mechanistic insights to the study of visual aesthetics and art appreciation
Visual link retrieval and knowledge discovery in painting datasets
Visual arts are of inestimable importance for the cultural, historic and economic growth of our society. One of the building blocks of most analysis in visual arts is to find similarity relationships among paintings of different artists and painting schools. To help art historians better understand visual arts, this paper presents a framework for visual link retrieval and knowledge discovery in digital painting datasets. Visual link retrieval is accomplished by using a deep convolutional neural network to perform feature extraction and a fully unsupervised nearest neighbor mechanism to retrieve links among digitized paintings. Historical knowledge discovery is achieved by performing a graph analysis that makes it possible to study influences among artists. An experimental evaluation on a database collecting paintings by very popular artists shows the effectiveness of the method. The unsupervised strategy makes the method interesting especially in cases where metadata are scarce, unavailable or difficult to collect
Fine Art Pattern Extraction and Recognition
This is a reprint of articles from the Special Issue published online in the open access journal Journal of Imaging (ISSN 2313-433X) (available at: https://www.mdpi.com/journal/jimaging/special issues/faper2020)
Deep learning approaches to pattern extraction and recognition in paintings and drawings: an overview
This paper provides an overview of some of the most relevant deep learning approaches to pattern extraction and recognition in visual arts, particularly painting and drawing. Recent advances in deep learning and computer vision, coupled with the growing availability of large digitized visual art collections, have opened new opportunities for computer science researchers to assist the art community with automatic tools to analyse and further understand visual arts. Among other benefits, a deeper understanding of visual arts has the potential to make them more accessible to a wider population, ultimately supporting the spread of culture
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active
research topic and has been successfully applied to various Artificial
Intelligence (AI) domains. As a potentially crucial technique for the
development of the next generation of emotional AI systems, we herein provide a
comprehensive overview of the application of adversarial training to affective
computing and sentiment analysis. Various representative adversarial training
algorithms are explained and discussed accordingly, aimed at tackling diverse
challenges associated with emotional AI systems. Further, we highlight a range
of potential future research directions. We expect that this overview will help
facilitate the development of adversarial training for affective computing and
sentiment analysis in both the academic and industrial communities
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