Geo-Semantic Segmentation

Abstract

The availability of GIS (Geographical Information System) databases for many urban areas, provides a valuable source of information for improving the performance of many computer vision tasks. In this paper, we propose a method which leverages information acquired from GIS databases to perform semantic segmentation of the image alongside with geo-referencing each semantic segment with its address and geo-location. First, the image is segmented into a set of initial super-pixels. Then, by projecting the information from GIS databases, a set of priors are obtained about the approximate location of the semantic entities such as buildings and streets in the image plane. However, there are significant inaccuracies (misalignments) in the projections, mainly due to inaccurate GPS-tags and camera parameters. In order to address this misalignment issue, we perform data fusion such that it improves the segmentation and GIS projections accuracy simultaneously with an iterative approach. At each iteration, the projections are evaluated and weighted in terms of reliability, and then fused with the super-pixel segmentations. First segmentation is performed using random walks, based on the GIS projections. Then the global transformation which best aligns the projections to their corresponding semantic entities is computed and applied to the projections to further align them to the content of the image. The iterative approach continues until the projections and segments are well aligned.

Publication Date

10-14-2015

Publication Title

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volume

07-12-June-2015

Number of Pages

2792-2799

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/CVPR.2015.7298896

Socpus ID

84959231601 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84959231601

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