Abstract
Scene labeling has been an important and popular area of computer vision and image processing for the past few years. It is the process of assigning pixels to specific predefined categories in an image. A number of techniques have been proposed for scene labeling but all have some limitations regarding accuracy and computational time. Some methods only incorporate the local context of images and ignore the global information of objects in an image. Therefore, accuracy of scene labeling is low for these methods. There is a need to address these issues of scene labeling to improve labeling accuracy. In this paper, we perform outdoor scene labeling using Automatic labeling Environment (ALE). We enhance this framework by incorporating bilateral filter based preprocessing, LSC superpixels and large co-occurrence weight. Experiments on a publicly available MSRC v1 dataset showed promising results with 89.44% pixel-wise accuracy and 78.02% class-wise accuracy.
Published Version
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