A. Chávez-Aragón, A. Medina Ma., O. Starostenko, and A. Zehe (Mexico)
image indexing, and retrieval, semantics, ontology.
This paper presents a new hybrid method of visual information retrieval, which combines low-level image analysis techniques such as a color model, principal corners detection approach with automatic indexing of the objects in image by ontological description of their textual annotations. The color and principal corner approaches have been selected, because they are invariant to rotation, scale, and illumination changes. The principal goal of proposed method is the integration of user-oriented descriptions, which provide more complete retrieval, accelerating the convergence to the expected result. For definition of the image semantics the ontology annotations of specific sub-regions obtained by CORPAI algorithm has been used in proposed method. Visual information retrieval is provided by comparison of the textual annotation generated by designed automatic facilities with input keywords selected by user. For implementation of the ontological annotation concepts, the image retrieval tool (IRONS system) has been designed and evaluated using Resource Description Framework language for establishment of machine readable semantics.
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