iccv iccv2013 iccv2013-299 iccv2013-299-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Michael Van_Den_Bergh, Gemma Roig, Xavier Boix, Santiago Manen, Luc Van_Gool
Abstract: Superpixel and objectness algorithms are broadly used as a pre-processing step to generate support regions and to speed-up further computations. Recently, many algorithms have been extended to video in order to exploit the temporal consistency between frames. However, most methods are computationally too expensive for real-time applications. We introduce an online, real-time video superpixel algorithm based on the recently proposed SEEDS superpixels. A new capability is incorporated which delivers multiple diverse samples (hypotheses) of superpixels in the same image or video sequence. The multiple samples are shown to provide a strong cue to efficiently measure the objectness of image windows, and we introduce the novel concept of objectness in temporal windows. Experiments show that the video superpixels achieve comparable performance to state-of-the-art offline methods while running at 30 fps on a single 2.8 GHz i7 CPU. State-of-the-art performance on objectness is also demonstrated, yet orders of magnitude faster and extended to temporal windows in video.
[1] B. Alexe, T. Deselaers, and V. Ferrari. Measuring the objectness of image windows. PAMI, 2012.
[2] P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik. Contour detection and hierarchical image segmentation. PAMI, 2011.
[3] A. Chen and J. Corso. Propagating multi-class pixel labels throughout video frames. In WNYIPW, 2010.
[4] M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL visual object classes (voc) challenge. IJCV, 2009.
[5] P. F. Felzenszwalb and D. P. Huttenlocher. Efficient graph-based image segmentation. International Journal of Computer Vision, 2004.
[6] J. Feng, Y. Wei, L. Tao, C. Zhang, and J. Sun. Salient object detection by composition. In ICCV, 2011.
[7] C. Fowlkes, S. Belongie, F. Chung, and J. Malik. Spectral grouping using the Nystrom method. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2004.
[8] M. Grundmann, V. Kwatra, M. Han, and I. Essa. Efficient hierarchical graph-based video segmentation. In CVPR, 2010.
[9] S. Paris. Edge-preserving smoothing and mean-shift segmentation of video streams. In ECCV, 2008.
[10] S. Paris and F. Durand. A topological approach to hierarchical segmentation using mean shift. In CVPR, 2007.
[11] E. Rahtu, J. Kannala, and M. B. Blaschko. Learning a category independent object detection cascade. In ICCV, 2011.
[12] E. Sharon, M. Galun, D. Sharon, R. Basri, and A. Brandt. Hierarchy
[13]
[14]
[15]
[16]
[17] and adaptivity in segmenting visual scenes. Nature, 2006. S. Stalder, H. Grabner, and L. V. Gool. Dynamic objectness for adaptive tracking. In Asian Conference on Computer Vision, 2012. K. E. A. van de Sande, J. R. R. Uijlings, T. Gevers, and A. W. M. Smeulders. Segmentation as selective search for object recognition. In ICCV, 2011. M. Van den Bergh, X. Boix, G. Roig, B. de Capitani, and L. Van Gool. SEEDS: Superpixels extracted via energy-driven sampling. In ECCV, 2012. C. Xu and J. Corso. Evaluation of super-voxel methods for early video processing. In CVPR, 2012. C. Xu, C. Xiong, and J. Corso. Streaming hierarchical video segmentation. In ECCV, 2012. 384