Abstract
In this paper, we propose a three-layer visual information processing architecture for extracting concise non-textual descriptions from visual contents. These coded descriptions capture both local saliencies and spatial configurations present in visual contents via prototypical visual tokens called visual “keywords”. Categorization of images and video shots represented by keyframes can be performed by comparing their coded descriptions. We demonstrate our proposed architecture in natural scene image categorization that outperforms methods which use aggregate measures of low-level features.
Real World Computing Partnership
Kent Ridge Digital Labs
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Lim, JH. (1999). Categorizing Visual Contents by Matching Visual “Keywords”. In: Huijsmans, D.P., Smeulders, A.W.M. (eds) Visual Information and Information Systems. VISUAL 1999. Lecture Notes in Computer Science, vol 1614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48762-X_46
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DOI: https://doi.org/10.1007/3-540-48762-X_46
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