TU Dortmund Department of Computer Science LS XII Pattern Recognition GroupPublications → Publication Details

On the Influence of Superpixel Methods for Image Parsing

Johann Strassburg, Rene Grzeszick, Leonard Rothacker and Gernot A. Fink
Proc. International Conference on Computer Vision Theory and Applications (VISAPP), 2015.

Image parsing describes a very fine grained analysis of natural scene images, where each pixel is assigned a label describing the object or part of the scene it belongs to. This analysis is a keystone to a wide range of applications that could benefit from detailed scene understanding, such as keyword based image search, sentence based image or video descriptions and even autonomous cars or robots. State-of-the art approaches in image parsing are data-driven and allow for recognizing arbitrary categories based on a knowledge transfer from similar images. As transferring labels on pixel level is tedious and noisy, more recent approaches build on the idea of segmenting a scene and transferring the information based on regions. For creating these regions the most popular approaches rely on over-segmenting the scene into superpixels. In this paper the influence of different superpixel methods will be evaluated within the well known Superparsing framework. Furthermore, a new method that computes a superpixel-like over-segmentation of an image is presented that computes regions based on edge-avoiding wavelets. The evaluation on the SIFT Flow and Barcelona dataset will show that the choice of the superpixel method is crucial for the performance of image parsing.

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