Efficient structured support vector regression

Support Vector Regression (SVR) has been a long standing

problem in machine learning, and gains its popularity on various

computer vision tasks. In this paper, we propose a structured support

vector regression framework by extending the max-margin principle to

incorporate spatial correlations among neighboring pixels. The objective

function in our framework considers both label information and pairwise

features, helping to achieve better cross-smoothing over neighboring

nodes. With the bundle method, we effectively reduce the number

of constraints and alleviate the adverse effect of outliers, leading to an

efficient and robust learning algorithm. Moreover, we conduct a thorough

analysis for the loss function used in structured regression, and

provide a principled approach for defining proper loss functions and deriving

the corresponding solvers to find the most violated constraint. We

demonstrate that our method outperforms the state-of-the-art regression

approaches on various testbeds of synthetic images and real-world scenes.