Ordinal regularized manifold feature extraction for image ranking

This paper proposes a novel feature extraction algorithm specifically designed for learning to rank in image ranking. Different from the previous works, the proposed method not only targets at preserving the local manifold structure of data, but also keeps the ordinal information among different data blocks in the low-dimensional subspace, where a ranking model can be learned effectively and efficiently. We first define the ideal directions of preserving local manifold structure and ordinal information, respectively. Based on the two definitions, a unified model is built to leverage the two kinds of information, which is formulated as an optimization problem. The experiments are conducted on two public available data sets: the MSRA-MM image data set and the “Web Queries” image data set, and the experimental results demonstrate the power of the proposed method against the state-of-the-art methods.

► Our work focused on feature extraction under learning to rank for image ranking. ► We considered preserving locality and ordinal information in low-dimensional subspace. ► We built a unified model to leverage the two kinds of information. ► We presented an efficient solution to the optimization problem.