The Community for Technology Leaders
Green Image
Issue No. 07 - July (2014 vol. 36)
ISSN: 0162-8828
pp: 1370-1383
Min Sun , University of Washington, AC101 Paul G. Allen Center, 185 Stevens Way, Seattle,
Byung-soo Kim , University of Michigan and the Computer Science Department, Stanford University, 353 Serra Mall, Gates Building, Stanford,
Pushmeet Kohli , Microsoft Research, , 21 Station Road, Cambridge CB1 2FB, United Kingdom
Silvio Savarese , Stanford University, , 353 Serra Mall, Gates Building, Stanford,
In the last few years, substantially different approaches have been adopted for segmenting and detecting “things” (object categories that have a well defined shape such as people and cars) and “stuff” (object categories which have an amorphous spatial extent such as grass and sky). While things have been typically detected by sliding window or Hough transform based methods, detection of stuff is generally formulated as a pixel or segment-wise classification problem. This paper proposes a framework for scene understanding that models both things and stuff using a common representation while preserving their distinct nature by using a property list. This representation allows us to enforce sophisticated geometric and semantic relationships between thing and stuff categories via property interactions in a single graphical model. We use the latest advances made in the field of discrete optimization to efficiently perform maximum a posteriori (MAP) inference in this model. We evaluate our method on the Stanford dataset by comparing it against state-of-the-art methods for object segmentation and detection. We also show that our method achieves competitive performances on the challenging PASCAL ’09 segmentation dataset.
Image segmentation, Semantics, Object detection, Shape, Electronic mail, Image color analysis, Detectors,graph-cut, Scene understanding, semantic labeling, segmentation
Min Sun, Byung-soo Kim, Pushmeet Kohli, Silvio Savarese, "Relating Things and Stuff via ObjectProperty Interactions", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 36, no. , pp. 1370-1383, July 2014, doi:10.1109/TPAMI.2013.193
184 ms
(Ver 3.3 (11022016))