Paper in WACV (2015): “Semantic Instance Labeling Leveraging Hierarchical Segmentation”


  • S. Hickson, I. Essa, and H. Christensen (2015), “Semantic Instance Labeling Leveraging Hierarchical Segmentation,” in Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV), 2015. [PDF] [DOI] [BIBTEX]
    @InProceedings{    2015-Hickson-SILLHS,
      author  = {Steven Hickson and Irfan Essa and Henrik
      booktitle  = {Proceedings of IEEE Winter Conference on
          Applications of Computer Vision (WACV)},
      doi    = {10.1109/WACV.2015.147},
      month    = {January},
      pdf    = {},
      publisher  = {IEEE Computer Society},
      title    = {Semantic Instance Labeling Leveraging Hierarchical
      year    = {2015}


Most of the approaches for indoor RGBD semantic labeling focus on using pixels or superpixels to train a classifier. In this paper, we implement a higher level segmentation using a hierarchy of superpixels to obtain a better segmentation for training our classifier. By focusing on meaningful segments that conform more directly to objects, regardless of size, we train a random forest of decision trees as a classifier using simple features such as the 3D size, LAB color histogram, width, height, and shape as specified by a histogram of surface normals. We test our method on the NYU V2 depth dataset, a challenging dataset of cluttered indoor environments. Our experiments using the NYU V2 depth dataset show that our method achieves state of the art results on both a general semantic labeling introduced by the dataset (floor, structure, furniture, and objects) and a more object specific semantic labeling. We show that training a classifier on a segmentation from a hierarchy of super pixels yields better results than training directly on super pixels, patches, or pixels as in previous work.2015-Hickson-SILLHS_pdf

Tags: , , , | Categories: Computer Vision, Henrik Christensen, PAMI/ICCV/CVPR/ECCV, Papers, Robotics, Steven Hickson | Date: January 6th, 2015 | By: Irfan Essa |

No Comments »

You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.

Leave a Reply