52,365 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
Adaptive Tag Selection for Image Annotation
Not all tags are relevant to an image, and the number of relevant tags is
image-dependent. Although many methods have been proposed for image
auto-annotation, the question of how to determine the number of tags to be
selected per image remains open. The main challenge is that for a large tag
vocabulary, there is often a lack of ground truth data for acquiring optimal
cutoff thresholds per tag. In contrast to previous works that pre-specify the
number of tags to be selected, we propose in this paper adaptive tag selection.
The key insight is to divide the vocabulary into two disjoint subsets, namely a
seen set consisting of tags having ground truth available for optimizing their
thresholds and a novel set consisting of tags without any ground truth. Such a
division allows us to estimate how many tags shall be selected from the novel
set according to the tags that have been selected from the seen set. The
effectiveness of the proposed method is justified by our participation in the
ImageCLEF 2014 image annotation task. On a set of 2,065 test images with ground
truth available for 207 tags, the benchmark evaluation shows that compared to
the popular top- strategy which obtains an F-score of 0.122, adaptive tag
selection achieves a higher F-score of 0.223. Moreover, by treating the
underlying image annotation system as a black box, the new method can be used
as an easy plug-in to boost the performance of existing systems
MESH: an object-oriented approach to hypermedia modeling and navigation.
This paper introduces the MESH approach to hypermedia modeling and navigation, which aims at relieving the typical drawbacks of poor maintainability and user disorientation. The framework builds upon two fundamental concepts. The data model combines established entity-relationship and object-oriented abstractions with proprietary concepts into a formal hypermedia data model. Uniform layout and link typing specifications can be attributed and inherited in a static node typing hierarchy, whereas both nodes and links can be submitted dynamically to multiple complementary classifications. In the context-based navigation paradigm, conventional navigation along static links is complemented by run-time generated guided tours, which are derived dynamically from the context of a user's information requirements. The result is a two-dimensional navigation paradigm, which reconciles complete navigational freedom and flexibility with a measure of linear guidance. These specifications are captured in a high-level, platform independent implementation framework.Data; Model; Specifications; Classification; Information; Requirements;
A context-based navigation paradigm for accessing web data.
This paper presents a context-based navigation paradigm, so as to overcome the phenomenon of user disorientation in a Web environment. Conventional navigation along static links is complemented by run-time generated guided tours, which are derived dynamically from the context of a user's information requirements. The result is a two-dimensional navigation paradigm, which reconciles complete navigational freedom and flexibility with a measure of linear guidance. Consequently, orientation is improved through reduced cognitive overhead and an increased sense of document coherence.Information; Requirements; Cognitive;
Digital image processing of the Ghent altarpiece : supporting the painting's study and conservation treatment
In this article, we show progress in certain image processing
techniques that can support the physical restoration of the painting, its art-historical analysis, or both. We show how analysis of the crack patterns could indicate possible areas of overpaint, which may be of great value for the physical restoration campaign, after further validation. Next, we explore how digital image inpainting can serve as a simulation for the restoration of paint losses. Finally, we explore how the statistical analysis of the relatively simple and frequently recurring objects (such as pearls in this masterpiece) may characterize the consistency of the painter’s style and thereby aid both art-historical interpretation and physical restoration campaign
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Abstraction and context in concept representation
This paper develops the notion of abstraction in the context of the psychology of concepts, and discusses its relation to context dependence in knowledge representation. Three general approaches to modelling conceptual knowledge from the domain of cognitive psychology are discussed, which serve to illustrate a theoretical dimension of increasing levels of abstraction
Learning Task Relatedness in Multi-Task Learning for Images in Context
Multimedia applications often require concurrent solutions to multiple tasks.
These tasks hold clues to each-others solutions, however as these relations can
be complex this remains a rarely utilized property. When task relations are
explicitly defined based on domain knowledge multi-task learning (MTL) offers
such concurrent solutions, while exploiting relatedness between multiple tasks
performed over the same dataset. In most cases however, this relatedness is not
explicitly defined and the domain expert knowledge that defines it is not
available. To address this issue, we introduce Selective Sharing, a method that
learns the inter-task relatedness from secondary latent features while the
model trains. Using this insight, we can automatically group tasks and allow
them to share knowledge in a mutually beneficial way. We support our method
with experiments on 5 datasets in classification, regression, and ranking tasks
and compare to strong baselines and state-of-the-art approaches showing a
consistent improvement in terms of accuracy and parameter counts. In addition,
we perform an activation region analysis showing how Selective Sharing affects
the learned representation.Comment: To appear in ICMR 2019 (Oral + Lightning Talk + Poster
A model-based approach to hypermedia design.
This paper introduces the MESH approach to hypermedia design, which combines established entity-relationship and object-oriented abstractions with proprietary concepts into a formal hypermedia data model. Uniform layout and link typing specifications can be attributed and inherited in a static node typing hierarchy, whereas both nodes and links can be submitted dynamically to multiple complementary classifications. In addition, the data model's support for a context-based navigation paradigm, as well as a platform-independent implementation framework, are briefly discussed.Data; Model; Specifications; Classification;
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