Abstract
Analyzing an image means extracting a number of relevant features for describing, meaningfully and concisely, image content. Usually, the selection of the features to be extracted depends on the image based task that has to be performed. This problem-dependency has caused the flourish of number and number of features in the literature, with a substantial disorganization of their introduction and definition. The idea behind the work reported in this paper is to make a step towards the systematization of the image feature domain by defining an ontological model, in which features and other concepts, relevant to feature definition and computation, are formally defined and catalogued. To this end, the Image Feature Ontology has been defined and is herein described. Such an ontology has the peculiarity of cataloguing features, modelling the image analysis domain and being integrated with a library of image processing algorithms, thus supplying functionalities for supporting feature selection and computation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Rodenacker, K., Bengtsson, E.: A feature set for cytometry on digitized microscopic images. Analytical Cellular Pathology 25, 1–36 (2003)
Colantonio, S., Perner, P., Salvetti, O.: Diagnosis of lymphatic tumours by case-based reasoning on microscopic images. Trans.on Case-Based Reas. 2(1), 29–40 (2009)
Viola, P., Jones, M.: Rapid object detection using boosted cascade of simple features. In: IEEE CVPR, pp. 511–518. IEEE Press, New York (2001)
Chandrasekhar, V., et al.: Comparison of local feature descriptors for mobile visual search. In: ICIP, pp. 3885–3888 (2010)
Gurevich, I.B., Koryabkina, I.V.: Comparative Analysis and Classification of Features for Image Models. Pattern Rec. and Image Analysis 16(3), 265–297 (2006)
Kalfoglou, Y., Schorlemmer, M.: IF-Map: An Ontology-Mapping Method Based on Information-Flow Theory. In: Spaccapietra, S., March, S., Aberer, K. (eds.) Journal on Data Semantics I. LNCS, vol. 2800, pp. 98–127. Springer, Heidelberg (2003)
Maillot, N., Thonnat, M.: Ontology-based complex object recognition. Image and Vision Comput. 26(1), 102–113 (2008)
Clouard, R., Renouf, A., Revenu, M.: Human-computer interaction for the generation of image processing applications. Int. J. of Human-Computer Studies 69(4), 201–219 (2011)
Hunter, J.: Adding Multimedia to the Semantic Web - Building an MPEG-7 Ontology. In: Proc. Int. Semantic Web Working Symposium (SWWS) Stanford, July 30-August 1 (2001)
Arndt, R., Troncy, R., Staab, S., Hardman, L., Vacura, M.: COMM: Designing a Well-Founded Multimedia Ontology for the Web. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 30–43. Springer, Heidelberg (2007)
Hollink, L., Little, S., Hunter, J.: Evaluating the Application of Semantic Inferencing Rules to Image Annotation. In: 3rd Int. Conf. on Know. Capt., KCAP 2005, Banff (October 2005)
Vacura, M., Svatek, V., Saathoff, C., Franz, T., Troncy, R.: Describing low-level image features using the COMM ontology. In: 15th IEEE Conf. on ICIP, pp. 49–52. IEEE Press (2008)
Asirelli, P., Little, S., Martinelli, M., Salvetti, O.: - Algorithm representation Use Case. In: Multimedia Annot. Interop. Framework. Multimedia Semantics Incubator Group (2006)
Colantonio, S., Salvetti, O., Gurevich, I.B., Trusova, Y.: An ontological framework for media analysis and mining. Pattern Rec. and Im. An. 19(2), 221–230 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Colantonio, S., Martinelli, M., Salvetti, O. (2012). Ontology and Algorithms Integration for Image Analysis. In: Salerno, E., Çetin, A.E., Salvetti, O. (eds) Computational Intelligence for Multimedia Understanding. MUSCLE 2011. Lecture Notes in Computer Science, vol 7252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32436-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-32436-9_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32435-2
Online ISBN: 978-3-642-32436-9
eBook Packages: Computer ScienceComputer Science (R0)