25,833 research outputs found
Craquelure as a Graph: Application of Image Processing and Graph Neural Networks to the Description of Fracture Patterns
Cracks on a painting is not a defect but an inimitable signature of an
artwork which can be used for origin examination, aging monitoring, damage
identification, and even forgery detection. This work presents the development
of a new methodology and corresponding toolbox for the extraction and
characterization of information from an image of a craquelure pattern.
The proposed approach processes craquelure network as a graph. The graph
representation captures the network structure via mutual organization of
junctions and fractures. Furthermore, it is invariant to any geometrical
distortions. At the same time, our tool extracts the properties of each node
and edge individually, which allows to characterize the pattern statistically.
We illustrate benefits from the graph representation and statistical features
individually using novel Graph Neural Network and hand-crafted descriptors
correspondingly. However, we also show that the best performance is achieved
when both techniques are merged into one framework. We perform experiments on
the dataset for paintings' origin classification and demonstrate that our
approach outperforms existing techniques by a large margin.Comment: Published in ICCV 2019 Workshop
edge2vec: Representation learning using edge semantics for biomedical knowledge discovery
Representation learning provides new and powerful graph analytical approaches
and tools for the highly valued data science challenge of mining knowledge
graphs. Since previous graph analytical methods have mostly focused on
homogeneous graphs, an important current challenge is extending this
methodology for richly heterogeneous graphs and knowledge domains. The
biomedical sciences are such a domain, reflecting the complexity of biology,
with entities such as genes, proteins, drugs, diseases, and phenotypes, and
relationships such as gene co-expression, biochemical regulation, and
biomolecular inhibition or activation. Therefore, the semantics of edges and
nodes are critical for representation learning and knowledge discovery in real
world biomedical problems. In this paper, we propose the edge2vec model, which
represents graphs considering edge semantics. An edge-type transition matrix is
trained by an Expectation-Maximization approach, and a stochastic gradient
descent model is employed to learn node embedding on a heterogeneous graph via
the trained transition matrix. edge2vec is validated on three biomedical domain
tasks: biomedical entity classification, compound-gene bioactivity prediction,
and biomedical information retrieval. Results show that by considering
edge-types into node embedding learning in heterogeneous graphs,
\textbf{edge2vec}\ significantly outperforms state-of-the-art models on all
three tasks. We propose this method for its added value relative to existing
graph analytical methodology, and in the real world context of biomedical
knowledge discovery applicability.Comment: 10 page
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