Learning to Rank Image Tags with Limited Training Examples
|Name||Learning to Rank Image Tags with Limited Training Examples|
Now a day image annotation has emerged as an important research topic due to its application in image matching and retrieval but still multi-label classification problem arise.We address this limitation by developing a novel approach that combines the strength of tag ranking with the power of matrix recovery. Instead of having to make a binary decision for each tag, our approach ranks tags in the descending order of their relevance to the given image, significantly simplifying the problem.the proposed method aggregates the prediction models for different tags into a matrix, and casts tag ranking into a matrix recovery problem. It introduces the matrix trace norm to explicitly control the model complexity, so that a reliable prediction model can be learned for tag ranking even when the tag space is large and the number of training images is limited.
|ieee paper year||2016|