Single image depth estimation from image descriptors

With the rapid emergence of 3D displays, we can enrich the user's viewing experiences by adding depth information to the widely existing 2D contents. However, effectively inferring the associated depth from a single 2D image is still a challenging problem. By taking benefits from the recently appeared image descriptors, we proposed the use of an SVM based framework for addressing the single image depth estimation. One advantage is its direct extension to incorporate the recent researches of large scale classification via SVM to meet the upcoming cloud computing paradigm. Our experimental results showed that the proposed framework outperforms the state-of-the-art approaches in performance, even the ones using more complex graphical models like MRF. Also, we made a brief investigation on the individual effectiveness of a set of commonly used image descriptors and found that spatial descriptors (e.g. texture) would be more effective than frequency ones (e.g. DCT coefficients).