Abstract
Several measures exist to describe similarities between digital contents, especially for what concerns images. Nevertheless, distances based on low-level visual features embedded in a multidimensional linear space are hardly suitable for capturing semantic similarities and recently novel techniques have been introduced making use of hierarchical knowledge bases. While being successfully exploited in specific contexts, the human perception of similarity cannot be easily encoded in such rigid structures. In this paper we propose to represent a knowledge base of semantic concepts as a complex network whose topology arises from free conceptual associations and is markedly different from a hierarchical structure. Images are anchored to relevant semantic concepts through an annotation process and similarity is computed following the related paths in the complex network. We finally show how this definition of semantic similarity is not necessarily restricted to images, but can be extended to compute distances between different types of sensorial information such as pictures and sounds, modeling the human ability to realize synaesthesias.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Albert, R., Barabási, A.L.: Statistical mechanics of complex networks. Reviews of Modern Physics 74(1), 47 (2002)
Barrat, A., Barthélemy, M., Vespignani, A.: Dynamical processes on complex networks, pp. 116–135. Cambridge University Press (2008)
Cattuto, C., Barrat, A., Baldassarri, A., Schehr, G., Loreto, V.: Collective dynamics of social annotation. Proceedings of the National Academy of Sciences 106(26), 10511–10515 (2009)
Collins, A.M., Loftus, E.F.: A spreading-activation theory of semantic processing. Psychological Review 82(6), 407–428 (1975)
Collins, A., Quillian, M.: Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behavior 8, 240–248 (1969)
Dall’Asta, L., Barrat, A., Barthélemy, M., Vespignani, A.: Vulnerability of weighted networks, March 2006. arXiv:physics/0603163v1
Datta, R., Li, J., Wang, J.Z.: Content-based image retrieval: approaches and trends of the new age. In: Zhang, H., Smith, J., Tian, Q. (eds.) Multimedia Information Retrieval, pp. 253–262. ACM (2005)
Deselaers, T., Ferrari, V.: Visual and semantic similarity in imagenet. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011, pp. 1777–1784. IEEE Computer Society, Washington, DC (2011)
Fang, C., Torresani, L.: Measuring image distances via embedding in a semantic manifold. In: European Conference on Computer Vision, pp. 402–415, October 2012
Gravino, P., Servedio, V.D.P., Barrat, A., Loreto, V.: Complex structures and semantics in free word association. Advances in Complex Systems 15(3–4) (2012)
Guarino, N., Oberle, D., Staab, S.: What is an ontology? In: Staab, S., Studer, R. (eds.) Handbook on Ontologies, 2nd edn. Springer (2009)
Kurtz, C., Beaulieu, C.F., Napel, S., Rubin, D.L.: A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations. J. of Biomedical Informatics 49(C), 227–244 (2014)
Markatopoulou, F., Mezaris, V., Kompatsiaris, I.: A comparative study on the use of multi-label classification techniques for concept-based video indexing and annotation. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds.) MMM 2014, Part I. LNCS, vol. 8325, pp. 1–12. Springer, Heidelberg (2014)
Miller, G., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.: Introduction to wordnet: an on-line lexical database. Int. J. Lexico. 3, 235–244 (1990)
Morais, A.S., Olsson, H., Schooler, L.: Mapping the structure of semantic memory. Cognitive Science 37, 125–145 (2012)
Nelson, D.L., McEvoy, C.L., Schreiber, T.A.: The university of south florida word association norms. http://w3.usf.edu/FreeAssociation
van Rijsbergen, K.: The Geometry of Information Retrieval. Cambridge University Press (2004–2007)
Roget, P.: Roget’s thesaurus of English words and phrases. TY Crowell Co. (1911)
Smeulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1349–1380 (2000)
Steyvers, M., Tenenbaum, J.B.: The large scale structure of semantic networks: Statistical analyses and a model of semantic growth. Cognitive Science 29, 41–78 (2005)
Tousch, A., Herbine, S., Audibert, J.: Semantic hierarchies for image annotation: a survey. Pattern Recognition 45, 333–345 (2012)
Gabler, K.: The human brain cloud. http://www.humanbraincloud.com
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Palumbo, E., Allasia, W. (2015). Semantic Similarity Between Images: A Novel Approach Based on a Complex Network of Free Word Associations. In: Amato, G., Connor, R., Falchi, F., Gennaro, C. (eds) Similarity Search and Applications. SISAP 2015. Lecture Notes in Computer Science(), vol 9371. Springer, Cham. https://doi.org/10.1007/978-3-319-25087-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-25087-8_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25086-1
Online ISBN: 978-3-319-25087-8
eBook Packages: Computer ScienceComputer Science (R0)