Laplacian Eigenmaps Regularized Feature Mapping for Image Annotation | IEEE Conference Publication | IEEE Xplore

Laplacian Eigenmaps Regularized Feature Mapping for Image Annotation


Abstract:

In the past two decades, researchers have shown great interest in automatic image annotation. However, most existing research methods do not consider similarities among s...Show More

Abstract:

In the past two decades, researchers have shown great interest in automatic image annotation. However, most existing research methods do not consider similarities among samples or do not obtain the suitable manifold information. Those methods also require the adequate and precise label sets. Considering above mentioned challenges, we propose a method, called laplacian eigenmaps regularized feature mapping for image annotation, which construct a laplacian matrix with all data in the training set (include labeled data and unlabeled data) and embed the laplacian matrix into feature mapping. Experimental results conducted on several benchmark image annotation datasets, such as Corel5K and ESP Game, demon-strate the effectiveness of the proposed method.
Date of Conference: 06-09 October 2019
Date Added to IEEE Xplore: 28 November 2019
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Conference Location: Bari, Italy

References

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