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BMVC
2010

Evaluation of dimensionality reduction methods for image auto-annotation

10 years 11 months ago
Evaluation of dimensionality reduction methods for image auto-annotation
Image auto-annotation is a challenging task in computer vision. The goal of this task is to predict multiple words for generic images automatically. Recent state-of-theart methods are based on a non-parametric approach that uses several visual features to calculate distances between image samples. While this approach is successful from the viewpoint of annotation accuracy, the computational costs, in terms of both complexity and memory use, tend to be high, since non-parametric methods require many training instances to be stored in memory to compute distances from a query. In this paper, we investigate several linear dimensionality reduction methods for efficient image annotation. Using the additional information provided by multiple labels, we can obtain a small representation preserving (and hopefully improving) the semantic distance of a visual feature. Linear methods are computationally reasonable and are suitable for practical large-scale systems, although only limited compariso...
Hideki Nakayama, Tatsuya Harada, Yasuo Kuniyoshi
Added 10 Feb 2011
Updated 10 Feb 2011
Type Journal
Year 2010
Where BMVC
Authors Hideki Nakayama, Tatsuya Harada, Yasuo Kuniyoshi
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