Nonparametric scene parsing in the images of buildings

Abstract

In this paper, we present a nonparametric approach to parse an image into regions of building, door, ground, sky, and other possible objects (such as cars, people, and trees). In a nonparametric method, first, similar images to that of the test are retrieved from a labeled training dataset. Then, the labels are transferred from the superpixels of the retrieved images to their corresponding superpixels of the test image. Finally, the conceptual Markov random field model is utilized to increase the superpixel labeling accuracy. In addition, we propose a method to improve door detection accuracy using the line, color, texture, and contextual cues. We have collected 3093 images of 40 different types of buildings from the LabelMe and Sun datasets, consisting of skyscrapers, shops, houses, apartments, churches, and so on. Experimental results on the dataset show the effectiveness of our approach with promising results.