5,790 research outputs found
Some Issues in the Art Image Database Systems
In this paper we illustrate several aspects of art databases, such as: the spread of the multimedia art images; the main characteristics of art images; main art images search models; unique characteristics for art image retrieval; the importance of the sensory and semantic gaps. In addition, we present several interesting features of an art image database, such as: image indexing; feature extraction; analysis on various levels of precision; style classification. We stress color features and their base, painting analysis and painting styles. We study also which MPEG-7 descriptors are best for fine painting images retrieval. An experimental system is developed to see how these descriptors work on 900 art images from several remarkable art periods. On the base of our experiments some suggestions for improving the process of searching and analysis of fine art images are given
Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark
Psychological research results have confirmed that people can have different
emotional reactions to different visual stimuli. Several papers have been
published on the problem of visual emotion analysis. In particular, attempts
have been made to analyze and predict people's emotional reaction towards
images. To this end, different kinds of hand-tuned features are proposed. The
results reported on several carefully selected and labeled small image data
sets have confirmed the promise of such features. While the recent successes of
many computer vision related tasks are due to the adoption of Convolutional
Neural Networks (CNNs), visual emotion analysis has not achieved the same level
of success. This may be primarily due to the unavailability of confidently
labeled and relatively large image data sets for visual emotion analysis. In
this work, we introduce a new data set, which started from 3+ million weakly
labeled images of different emotions and ended up 30 times as large as the
current largest publicly available visual emotion data set. We hope that this
data set encourages further research on visual emotion analysis. We also
perform extensive benchmarking analyses on this large data set using the state
of the art methods including CNNs.Comment: 7 pages, 7 figures, AAAI 201
Analysis of the Distributions of Color Characteristics in Art Painting Images
In this paper we study some of the characteristics of the art
painting image color semantics. We analyze the color features of differ-
ent artists and art movements. The analysis includes exploration of hue,
saturation and luminance. We also use quartile’s analysis to obtain the dis-
tribution of the dispersion of defined groups of paintings and measure the
degree of purity for these groups. A special software system “Art Paint-
ing Image Color Semantics” (APICSS) for image analysis and retrieval was
created. The obtained result can be used for automatic classification of art
paintings in image retrieval systems, where the indexing is based on color
characteristics
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
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
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