HFS: Hierarchical Feature Selection for Efficient Image Segmentation

  • Ming-Ming Cheng
  • Yun Liu
  • Qibin Hou
  • Jiawang Bian
  • Philip Torr
  • Shi-Min Hu
  • Zhuowen Tu
Conference paper

DOI: 10.1007/978-3-319-46487-9_53

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9907)
Cite this paper as:
Cheng MM. et al. (2016) HFS: Hierarchical Feature Selection for Efficient Image Segmentation. In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision – ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol 9907. Springer, Cham

Abstract

In this paper, we propose a real-time system, Hierarchical Feature Selection (HFS), that performs image segmentation at a speed of 50 frames-per-second. We make an attempt to improve the performance of previous image segmentation systems by focusing on two aspects: (1) a careful system implementation on modern GPUs for efficient feature computation; and (2) an effective hierarchical feature selection and fusion strategy with learning. Compared with classic segmentation algorithms, our system demonstrates its particular advantage in speed, with comparable results in segmentation quality. Adopting HFS in applications like salient object detection and object proposal generation results in a significant performance boost. Our proposed HFS system (will be open-sourced) can be used in a variety computer vision tasks that are built on top of image segmentation and superpixel extraction.

Keywords

Image segmentation Superpixel Grouping 

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Ming-Ming Cheng
    • 1
  • Yun Liu
    • 1
  • Qibin Hou
    • 1
  • Jiawang Bian
    • 1
  • Philip Torr
    • 3
  • Shi-Min Hu
    • 2
  • Zhuowen Tu
    • 4
  1. 1.CCCE & CSNankai UniversityTianjinChina
  2. 2.Tsinghua UniversityBeijingChina
  3. 3.Oxford UniversityOxfordUK
  4. 4.UCSDSan DiegoUSA

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