IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust analysis of feature spaces: color image segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Video retrieval using spatio-temporal descriptors
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Image-Segmentation Evaluation From the Perspective of Salient Object Extraction
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Distance measures for image segmentation evaluation
EURASIP Journal on Applied Signal Processing
Graph-Based spatio-temporal region extraction
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Image sequence analysis for emerging interactive multimedia services-the European COST 211 framework
IEEE Transactions on Circuits and Systems for Video Technology
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Spatial region (image) segmentation is a fundamental step for many computer vision applications. Although many methods have been proposed, less work has been done in developing suitable evaluation methodologies for comparing different approaches. The main problem of general purpose segmentation evaluation is the dilemma between objectivity and generality. Recently, figure ground segmentation evaluation has been proposed to solve this problem by defining an unambiguous ground truth using the most salient foreground object. Although the annotation of a single foreground object is less complex than the annotation of all regions within an image, it is still quite time consuming, especially for videos. A novel framework incorporating background subtraction for automatic ground truth generation and different foreground evaluation measures is proposed, that allows to effectively and efficiently evaluate the performance of image segmentation approaches. The experiments show that the objective measures are comparable to the subjective assessment and that there is only a slight difference between manually annotated and automatically generated ground truth.