A Hybrid Method for Distance Metric Learning
This work addresses distance metric learning for applications like medical image retrieval, but it appears incremental as it combines existing approaches.
The paper tackles the problem of learning a distance metric by proposing a hybrid method that uses both similarity ratings and class labels, showing significant improvement in retrieval performance on synthetic and real medical image data.
We consider the problem of learning a measure of distance among vectors in a feature space and propose a hybrid method that simultaneously learns from similarity ratings assigned to pairs of vectors and class labels assigned to individual vectors. Our method is based on a generative model in which class labels can provide information that is not encoded in feature vectors but yet relates to perceived similarity between objects. Experiments with synthetic data as well as a real medical image retrieval problem demonstrate that leveraging class labels through use of our method improves retrieval performance significantly.