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

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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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Data availability

The prototype system and the dental x-ray SVG dataset generated and analyzed during the current study are available online at: http://sigappfr.acm.org/Projects/SSG/.

Notes

  1. https://www.images.google.com.

  2. https://www.Flickr.com.

  3. https://www.picsearch.com/.

  4. https://www.w3.org/TR/SVG2/.

  5. https://www.w3.org/RDF/.

  6. Available at: https://code.google.com/p/svg-edit/.

  7. Available at: http://www.janvas.com/site/home_en.php.

  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.

  11. Available online at: http://sigappfr.acm.org/Projects/SSG/.

  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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Authors and Affiliations

  1. Computer Science and Engineering Department, American University of Ras Al Khaimah (AURAK), 10021, Ras Al Khaimah, United Arab Emirates

    Khouloud Salameh

  2. Electrical and Computer Engineering Department, Lebanese American University (LAU), Byblos, 36, Lebanon

    Farah El Akoum & Joe Tekli

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Contributions

We confirm the authors’ contributions in this work and their distribution of the tasks as follows: –Khouloud Salameh (co-supervisor): Conceptualization, Formal Analysis, Data Curation, Validation, Writing-Original Draft, Supervision. –Farah El Akoum (B.E. graduate): Conceptualization, Software, Data Curation, Validation, Writing-Original Draft. –Joe Tekli (supervisor): Methodology, Formal Analysis, Writing-Original Draft, Writing-Review and Editing, Visualization, Supervision, Project administration.

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Salameh, K., Akoum, F.E. & Tekli, J. Unsupervised knowledge representation of panoramic dental X-ray images using SVG image-and-object clustering. Multimedia Systems 29, 2293–2322 (2023). https://doi.org/10.1007/s00530-023-01099-6

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