Real-time semantic context labeling for image understanding

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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 languageEnglish
Title of host publicationProceedings of the2015 IEEE International Conference on Image Processing (ICIP 2015), 27-30 September 2015, Quebec City, Canada
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Print)978-1-4799-8339-1
Publication statusPublished - 2015
Event22nd IEEE International Conference on Image Processing (ICIP 2015) - Quebec, Canada
Duration: 27 Sept 201530 Sept 2015
Conference number: 22


Conference22nd IEEE International Conference on Image Processing (ICIP 2015)
Abbreviated titleICIP 2015
Internet address


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