Abstract

Inferring depth from a single image is a difficult task in com- puter vision, which needs to utilize adequate monocular cues contained in the image. Inspired by Saxena et al’s work, this paper presents a close- form iterative algorithm to process multi-scale image segmentation and depth inferring alternately, which can significantly improve segmentation and depth estimate results. First, an EM-based algorithm is applied to obtain an initial multi-scale image segmentation result. Then, the multi- scale Markov random field (MRF) model, trained by supervised learning, is used to infer both depths and the relations between depths at different image regions. Next, a graph-based region merging algorithm is applied to merge the segmentations at the larger scales by incorporating the in- ferred depths. At the last, the refined multi-scale image segmentations are used as input of MRF model and the depth are re-inferred. The above processes are iteratively continued until the expected results are achieved. Since there are no changes on the segmentations at the finest scale in the iterative process, it still can capture the detailed 3D struc- ture. Meanwhile, the refined segmentations at the other scales will help obtain more global structure information in the image. The contrastive experimental results verify the validity of our method that it can infer quantitatively better depth estimations for 62.7% of 134 images down- loaded from the Saxena’s database. Our method can also improve the image segmentation results in the sense of scene interpretation. More- over, the paper extends the method to estimate the depth of the scene with fore-ob jects.