Abstract
Automatic detection approaches depend essentially on the use of classifiers, that in turn are based on the learning of a given training set. The choice of the training data is crucial: even if this aspect is often neglected, the visual information contained in the training samples can make the difference in a detection/classification scenario. A good training set has to be sufficiently informative to capture the nature of the object under analysis, but at the same time has to be generic enough to avoid overfitting and to cope with new instances of the object of interest. In this paper we follow those approaches that pursue automatic learning from Internet data. We try to show how such training set can be made more appropriate by leveraging on semantic technologies, like lexical resources and ontologies, in the task of retrieving images from the Web through the use of a search engine. Experiments on several object classes of the CalTech101 dataset promote our idea, showing an average increment on the detection accuracy of about 8%.
Chapter PDF
References
Bunescu, R., Mooney, R.: Multiple instance learning for sparse positive bags. In: ICML 2007, pp. 105–112. ACM, New York (2007)
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. Comput. Vis. Image Underst. 106(1), 59–70 (2007)
Fellbaum, C.: Wordnet and wordnets. In: Brown, K. (ed.) Encyclopedia of Language and Linguistics, pp. 665–670. Elsevier, Oxford (2005)
Helmer, S., Meger, D., Viswanathan, P., McCann, S., Dockrey, M., Fazli, P., Southey, T., Muja, M., Joya, M., Little, J., Lowe, D., Mackworth, A.: Semantic robot vision challenge: Current state and future directions. CoRR (2009)
Lopes, L.S., Chauhan, A.: How many words can my robot learn? an approach and experiments with one-class learning. Interaction Studies 8, 53–81 (2007)
Masolo, C., Borgo, S., Gangemi, A., Guarino, N., Oltramari, A.: Wonderweb deliverable d18. Tech. rep., CNR (2003)
Meger, D., Muja, M., Helmer, S., Gupta, A., Gamroth, C., Hoffman, T., Baumann, M., Southey, T., Fazli, P., Wohlkinger, W., Viswanathan, P., Little, J., Lowe, D., Orwell, J.: Curious george: An integrated visual search platform. In: CRV, pp. 107–114 (2010)
Popescu, A., Millet, C., Moëllic, P.A.: Ontology driven content based image retrieval. In: CIVR, pp. 387–394 (2007)
Prévot, L., Borgo, S., Oltramari, A.: Ontology and the lexicon: a multi-disciplinary perspective (introduction). Studies in Natural Language Processing, pp. 185–200. Cambridge University Press (April 2010)
Roy, D., Pentland, A.: Learning words from sights and sounds: A computational model (1999), http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.9295
Schroff, F., Criminisi, A., Zisserman, A.: Harvesting image databases from the web. In: ICCV (2007)
Setz, A.T., Snoek, C.G.M.: Can social tagged images aid concept-based video search? In: ICME (2009)
Steels, L., Kaplan, F.: Aibo’s first words: The social learning of language and meaning. Evol. of Communication 4(1), 3–32 (2001)
Ulges, A., Schulze, C., Koch, M., Breuel, T.: Learning automatic concept detectors from online video. Comput. Vis. Image Underst. 114(4), 429–438 (2010)
Vijayanarasimhan, S., Grauman, K.: Keywords to visual categories: Multiple-instance learning for weakly supervised object categorization. In: CVPR (2008)
Yeh, T., Darrell, T.: Dynamic visual category learning. In: CVPR (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Setti, F., Cheng, DS., Abdulhak, S.A., Ferrario, R., Cristani, M. (2013). Ontology-Assisted Object Detection: Towards the Automatic Learning with Internet. In: Petrosino, A. (eds) Image Analysis and Processing – ICIAP 2013. ICIAP 2013. Lecture Notes in Computer Science, vol 8157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41184-7_20
Download citation
DOI: https://doi.org/10.1007/978-3-642-41184-7_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41183-0
Online ISBN: 978-3-642-41184-7
eBook Packages: Computer ScienceComputer Science (R0)