Abstract
The optimal solution to support a great annotator is very difficult to derive. Up to the present, little work can precisely tag the images since the so-called semantic gap is not easy to reduce. This paper constitutes the approach of approximating the solution to discover the visual-concept associations from the image-concept distribution. The experimental results show that our proposed annotation approach is very effective in facing the diverse relations between visual features and human concepts.
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Ying, M., Laomo, Z., Jixun, G. (2012). A Novel Image Semantic Annotation Method Based on Image-Concept Distribution Model. In: Luo, J. (eds) Affective Computing and Intelligent Interaction. Advances in Intelligent and Soft Computing, vol 137. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27866-2_36
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DOI: https://doi.org/10.1007/978-3-642-27866-2_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27865-5
Online ISBN: 978-3-642-27866-2
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