Ordinal Distance Metric Learning with MDS for Image Ranking
This work addresses image ranking for computer vision applications, but it is incremental as it builds on existing linear distance metric learning methods.
The paper tackles image ranking by proposing an improved linear ordinal distance metric learning approach that maintains data structures and ordinal relations via multidimensional scaling, achieving better speed and ranking performance than the baseline model.
Image ranking is to rank images based on some known ranked images. In this paper, we propose an improved linear ordinal distance metric learning approach based on the linear distance metric learning model. By decomposing the distance metric $A$ as $L^TL$, the problem can be cast as looking for a linear map between two sets of points in different spaces, meanwhile maintaining some data structures. The ordinal relation of the labels can be maintained via classical multidimensional scaling, a popular tool for dimension reduction in statistics. A least squares fitting term is then introduced to the cost function, which can also maintain the local data structure. The resulting model is an unconstrained problem, and can better fit the data structure. Extensive numerical results demonstrate the improvement of the new approach over the linear distance metric learning model both in speed and ranking performance.