THESIS
2019
xi, 42 pages : illustrations (some color) ; 30 cm
Abstract
With advances in artificial intelligence (AI) and the availability of comprehensive digital
copies of visual art, analyzing visual arts automatically becomes feasible. Extracting
discriminative representations of visual art plays a key role in visual arts analytics, which
is different from normal image analytics and has different sets of challenges. In normal
images, the semantic concepts are usually regarded as powerful representations. However,
in visual arts images, the information is conveyed by the subject matter and the
style. Style is not necessarily correlated with subject matter, which corresponds to the
existence of certain objects or contents in the images. Therefore, a unified framework
is proposed in this thesis to learn joint representations that can simultaneously...[
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With advances in artificial intelligence (AI) and the availability of comprehensive digital
copies of visual art, analyzing visual arts automatically becomes feasible. Extracting
discriminative representations of visual art plays a key role in visual arts analytics, which
is different from normal image analytics and has different sets of challenges. In normal
images, the semantic concepts are usually regarded as powerful representations. However,
in visual arts images, the information is conveyed by the subject matter and the
style. Style is not necessarily correlated with subject matter, which corresponds to the
existence of certain objects or contents in the images. Therefore, a unified framework
is proposed in this thesis to learn joint representations that can simultaneously capture
the content and style of visual art. In the real world, many applications (e.g., visual
arts recognition, retrieval, recommendation, etc.) can be built based on the proposed
learned representations of visual art. However, in most scenarios, the performance of
the proposed framework will suffer from different issues (e.g., distortions, blur) existing
in the images, especially for mobile-based applications. To overcome this problem, an
image rectification pipeline with three modules is designed to rectify the images. The
effectiveness of the proposed methods has been proved through a series of experiments.
Additionally, a real-world system is implemented
1 and two visual arts datasets
2 are
released to facilitate research in this area.
1https://deepart2.ece.ust.hk
2https://deepart2.ece.ust.hk/ART500K/art500k.html
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