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CVPR
2009
IEEE

Learning Semantic Visual Vocabularies Using Diffusion Distance

14 years 9 months ago
Learning Semantic Visual Vocabularies Using Diffusion Distance
In this paper, we propose a novel approach for learning generic visual vocabulary. We use diffusion maps to au-tomatically learn a semantic visual vocabulary from ab-undant quantized midlevel features. Each midlevel feature is represented by the vector of pointwise mutual informa-tion (PMI). In this midlevel feature space, we believe the features produced by similar sources must lie on a certain manifold. To capture the intrinsic geometric relations be-tween features, we measure their dissimilarity using diffu-sion distance. The underlying idea is to embed the midlevel features into a semantic lower-dimensional space. Our goal is to construct a compact yet discriminative semantic visual vocabulary. Although the conventional approach using k-means is good for vocabulary construction, its performance is sen-sitive to the size of the visual vocabulary. In addition, the learnt visual words are not semantically meaningful since the clustering criterion is based on appearance similarity onl...
Jingen Liu (University of Central Florida), Yang Y
Added 06 May 2009
Updated 10 Dec 2009
Type Conference
Year 2009
Where CVPR
Authors Jingen Liu (University of Central Florida), Yang Yang (University of Central Florida), Mubarak Shah (University of Central Florida)
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