Abstract
The use of context information in a scene is an important aid for full semantic scene understanding in security and surveillance applications. To this end, this paper presents an innovative semantic context-labeling algorithm for three context classes, trading-off quality and real-time execution. Our system consists of three consecutive stages: image segmentation, region-based feature extraction and classification. We propose the joint use of the features color in HSV space, texture from Gabor filters and spatial context, in combination with the Directional Nearest Neighbor (DNN) method for constructing the undirected graph for segmentation. Compared to recent literature, this combination is over 35 times faster and achieves a coverability rate that is 65% higher.
Original language | English |
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Title of host publication | Proceedings of the2015 IEEE International Conference on Image Processing (ICIP 2015), 27-30 September 2015, Quebec City, Canada |
Place of Publication | Piscataway |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 3180-3184 |
ISBN (Print) | 978-1-4799-8339-1 |
DOIs | |
Publication status | Published - 2015 |
Event | 22nd IEEE International Conference on Image Processing (ICIP 2015) - Quebec, Canada Duration: 27 Sept 2015 → 30 Sept 2015 Conference number: 22 http://www.icip2015.org/ |
Conference
Conference | 22nd IEEE International Conference on Image Processing (ICIP 2015) |
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Abbreviated title | ICIP 2015 |
Country/Territory | Canada |
City | Quebec |
Period | 27/09/15 → 30/09/15 |
Internet address |