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Multimedia Systems
Published in: Multimedia Systems 4/2023

24-05-2023 | Regular Paper

Unsupervised knowledge representation of panoramic dental X-ray images using SVG image-and-object clustering

Authors: Khouloud Salameh, Farah El Akoum, Joe Tekli

Published in: Multimedia Systems | Issue 4/2023

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Abstract

Given that the meaning of an image is rarely self-evident using traditional keyword and/or content-based descriptions, the general goal of this study is to convert, with minimal human intervention, a stream of web vector graphics into a searchable knowledge graph structure that encodes semantically relevant image contents. To do so, we introduce an original framework titled SSG which automatically converts a stream of SVG images and objects into a semantic graph. We introduce an incremental clustering approach to semantically annotate SVG images and their constituent objects in a fast and efficient manner, using an aggregation of shape, area, color, and location similarity measures. We then produce an RDF graph representation of the input image and integrate it in a reference knowledge graph, incrementally extending its semantic expressiveness to improve future annotation tasks. This achieves semantization of vector image contents with minimum human effort and training data, while complying with native Web standards (i.e., SVG and RDF) to preserve transparency in representing and searching images using Semantic Web stack technologies. Our solution is of linear complexity in the number of images and clusters used. We have conducted a large battery of experiments to test and evaluate our approach. We have created a labelled SVG dataset consisting of 22,553 objects from 750 images based on panoramic dental X-ray images. To our knowledge, it is the first significant dataset of labelled SVG objects and images, which we make available online as a benchmark for future research in this area. Results underline our approach’s effectiveness, and its applicability in a practical application domain.
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Footnotes
8
In an RDF subject-predicate-object expression, the subject denotes the resource being described, the predicate denotes a trait or aspect of the resource, expressing a relation between the subject and the object, where the object designates another resource or a data value [76].
 
9
\({\text{Sim}}_{{{Lin}}} {\text{(c}}_{{1}} {\text{, c}}_{{2}} {\text{, CO) = }}\frac{{{2} \times {\text{log p(c}}_{{0}} {)}}}{{{\text{log p(c}}_{{1}} {\text{) + log p(c}}_{{2}} {)}}} \, \in \left[ {0,{1}} \right]\)
where CO designates a reference hierarchical color ontology, N1 and N2 are respectively the lengths of the paths separating colors c1 and c2 from their lowest common ancestor color c0 in CO, and N0 is the length of the path separating color c0 from the root of CO [87].
 
10
\({\text{Sim}}_{{{\text{WuPalmer}}}} {\text{(c}}_{{1}} {\text{, c}}_{{2}} {\text{, CO) = }}\frac{{{2} \times {N}_{{0}} }}{{{N}_{{1}} { + N}_{{2}} { + 2} \times {N}_{{0}} }} \, \in \left[ {0,{1}} \right]\)
where p(Ci) denotes the occurrence probability of color ci designating the frequency of occurrence of the name color ci in a reference corpus [88], such as the Brown text corpus [89] adopted in our study.
 
12
We adopt Noe4j as a graph database to represent our semantic graphs, versus using a native RDF representation in Protégé [93] for instance, due to the latter’s significant efficiency and processing speed compared with the latter. Other graph databases can be used as plug-and-play models according to the admin’s preferences.
 
13
The complete knowledge graph is available online on the project prototype Web page.
 
14
The cosine measure only detects variations in vector angles, which highlight differences between the feature vectors’ directions. This is commonly adopted in image annotation and retrieval where images of the same label might have significant feature vector module variations while sharing similar vector directions. This is especially useful with high-dimensional data where vector similarity is commonly evaluated as the similarity between the vectors’ directions, versus vector module similarity which is considered to be too specific and oftentimes misleading especially with higher dimensionality, e.g. [94, 97].
 
15
Graphical user interface.
 
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Metadata
Title
Unsupervised knowledge representation of panoramic dental X-ray images using SVG image-and-object clustering
Authors
Khouloud Salameh
Farah El Akoum
Joe Tekli
Publication date
24-05-2023
Publisher
Springer Berlin Heidelberg
Published in
Multimedia Systems / Issue 4/2023
Print ISSN: 0942-4962
Electronic ISSN: 1432-1882
DOI
https://doi.org/10.1007/s00530-023-01099-6

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