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Region-based Image Annotation using Asymmetrical Support Vector Machine-based Multiple-Instance Learning

8 years 11 months ago
Region-based Image Annotation using Asymmetrical Support Vector Machine-based Multiple-Instance Learning
In region-based image annotation, keywords are usually associated with images instead of individual regions in the training data set. This poses a major challenge for any learning strategy. In this paper, we formulate image annotation as a supervised learning problem under Multiple-Instance Learning (MIL) framework. We present a novel Asymmetrical Support Vector Machine-based MIL algorithm (ASVM-MIL), which extends the conventional Support Vector Machine (SVM) to the MIL setting by introducing asymmetrical loss functions for false positives and false negatives. The proposed ASVM-MIL algorithm is evaluated on both image annotation data sets and the benchmark MUSK data sets.
Changbo Yang, Ming Dong, Jing Hua
Added 22 Aug 2010
Updated 22 Aug 2010
Type Conference
Year 2006
Where CVPR
Authors Changbo Yang, Ming Dong, Jing Hua
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