NDLI: Improving spatial codification in semantic segmentation

Content Provider	IEEE Xplore Digital Library
Author	Ventura, C. Giro-i-Nieto, X. Vilaplana, V. McGuinness, K. Marques, F. O'Connor, N.E.
Copyright Year	2015
Description	Author affiliation: Univ. Politec. de Catalunya, Barcelona, Spain (Ventura, C.; Giro-i-Nieto, X.; Vilaplana, V.; Marques, F.) \|\| Insight Centre for Data Analytics, Dublin City Univ., Dublin, Ireland (McGuinness, K.; O'Connor, N.E.)
Abstract	This paper explores novel approaches for improving the spatial codification for the pooling of local descriptors to solve the semantic segmentation problem. We propose to partition the image into three regions for each object to be described: Figure, Border and Ground. This partition aims at minimizing the influence of the image context on the object description and vice versa by introducing an intermediate zone around the object contour. Furthermore, we also propose a richer visual descriptor of the object by applying a Spatial Pyramid over the Figure region. Two novel Spatial Pyramid configurations are explored: Cartesian-based and crown-based Spatial Pyramids. We test these approaches with state-of-the-art techniques and show that they improve the Figure-Ground based pooling in the Pascal VOC 2011 and 2012 semantic segmentation challenges.
Starting Page	3605
Ending Page	3609
File Size	899386
Page Count	5
File Format	PDF
e-ISBN	9781479983391
DOI	10.1109/ICIP.2015.7351476
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Semantics Image segmentation Visualization Training Feature extraction Proposals Spatial codification Semantic segmentation Object recognition Object segmentation
Content Type	Text
Resource Type	Article

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