HFS: Hierarchical Feature Selection for Efficient Image Segmentation Ming-Ming Cheng1 Yun Liu1 Qibin Hou1 Jiawang Bian1 Philip Torr2 Shi-Min Hu3 Zhuowen Tu4 1CCCE&CS, Nankai University 2Oxford University 3Tsinghua University 4UCSD Abstract Boundary Evaluation We propose a real-time system, Hierarchical Feature Selection (HFS), that performs high quality image segmentation at a speed of 50 fps. Major improvements includes: a careful system implementation on modern GPUs for efficient feature computation; an effective hierarchical feature selection and fusion strategy with learning. Significant performance boost has been demonstrated in applications such as salient object detection and object proposal generation. Hierarchical Feature Selection (HFS) Region Evaluation Applications Workflow Objectness [10] Saliency [11] Object dectection [12] 3D inference [13] Tracking [14] Sample Results [1] Slic superpixels compared to state-of-the-art superpixel methods. IEEE TPAMI,2012 [2] Contour detection and hierarchical image segmentation. IEEE TPAMI, 2011 [3] Multiscale combinatorial grouping. IEEE CVPR, 2014 [4] Mean shift: A robust approach toward feature space analysis. IEEE TPAMI, 2002 [5] Spectral segmentation with multiscale graph decomposition. IEEE CVPR, 2005 [6] Ecient graph-based image segmentation. IJCV, 2004 [7] Fast and eective l0 gradient minimization by region fusion. IEEE ICCV, 2015 [8] gslicr: Slic superpixels at over 250hz. arXiv, 2015 [9] Fast partitioning of vector-valued images. SIAM Journal on Imaging Sciences, 2014 [10] Improving object proposals with multithresholding straddling expansion. IEEE CVPR, 2015 [11] Salient object detection: A discriminative regional feature integration approach. IEEE CVPR, 2013 [12] Robust Higher Order Potentials for Enforcing Label Consistency. IEEE CVPR, 2008 [13] Geometric Context from a Single Image. ICCV, 2005 [14] Superpixel Tracking. ICCV, 2011 free! http://mmcheng.net/hfs/