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Mid-level concept learning with visual contextual ontologies and probabilistic inference for image annotation

Liu, Yuee, Zhang, Jinglan, Tjondronegoro, Dian W., Geva, Shlomo, & Li, Zhengrong (2010) Mid-level concept learning with visual contextual ontologies and probabilistic inference for image annotation. In The 16th International Multimedia Modeling Conference, 6-8 January 2010, Haiyu Hotspring Hotel, Chongqing. (In Press)

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Abstract

To date, automatic recognition of semantic information such as salient objects and mid-level concepts from images is a challenging task. Since real-world objects tend to exist in a context within their environment, the computer vision researchers have increasingly incorporated contextual information for improving object recognition. In this paper, we present a method to build a visual contextual ontology from salient objects descriptions for image annotation. The ontologies include not only partOf/kindOf relations, but also spatial and co-occurrence relations. A two-step image annotation algorithm is also proposed based on ontology relations and probabilistic inference. Different from most of the existing work, we specially exploit how to combine representation of ontology, contextual knowledge and probabilistic inference. The experiments show that image annotation results are improved in the LabelMe dataset.

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ID Code: 27942
Item Type: Conference Paper
Additional Information: This paper is accepted and will be published in MMM2010 Conference proceedings by Springer as LNCS 5916.
Keywords: Image Annotation, Salient Objects, Visual Context, Ontology, Probabilistic Inference, multi-level concept
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2010 [please consult the authors]
Deposited On: 14 Oct 2009 12:49
Last Modified: 18 Jul 2014 13:50

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