Sparse coding and mid-level superpixel-feature for ℓ0-graph based unsupervised image segmentation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8048 LNCS, Issue PART 2, 2013, Pages 160-168

  Wang, Xiaofang ^a   Li, Huibin ^a   Masnou, Simon ^b   Chen, Liming ^a  

^a ECOLE CENTRALE DE LYON   (France)

^b UNIVERSITÉ LYON 1   (France)

Author keywords

0 graph; image segmentation; mid level features; sparse coding; superpixels

Indexed keywords

LOCAL FEATURE DETECTORS; MID-LEVEL FEATURES; SPARSE CODING; SPARSE REPRESENTATION; STATE-OF-THE-ART METHODS; SUPERPIXELS; UNSUPERVISED IMAGE SEGMENTATION; UNSUPERVISED SEGMENTATION;

FEATURE EXTRACTION; GRAPHIC METHODS; IMAGE ANALYSIS; IMAGE SEGMENTATION;

GRAPH THEORY;

EID: 84884491077     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40246-3_20     Document Type: Conference Paper

References (18)

1. Cheng, B., Liu, G., Wang, J., Huang, Z., Yan, S.: Multi-task low-rank affinity pursuit for image segmentation. In: ICCV, pp. 2439-2446 (2011)
2. Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. PAMI 24(5), 603-619 (2002)
3. Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. IJCV 59(2), 167-181 (2004)
4. Freixenet, J., Muñoz, X., Raba, D., Martí, J., Cufí, X.: Yet another survey on image segmentation: Region and boundary information integration. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part III. LNCS, vol. 2352, pp. 408-422. Springer, Heidelberg (2002)
5. Fu, H., Qiu, G.: Integrating low-level and semantic features for object consistent segmentation. In: Int. Conf. on Image and Graphics (ICIG), pp. 39-44 (2011)
6. Lee, Y.J., Grauman, K.: Object-graphs for context-aware visual category discovery. PAMI 34(2), 346-358 (2012)
7. Li, L.J., Su, H., Xing, E.P., Fei-Fei, L.: Object bank: A high-level image representation for scene classification and semantic feature sparsification. Advances in Neural Information Processing Systems (2010)
8. Li, Z.,Wu, X.M., Chang, S.F.: Segmentation using superpixels: A bipartite graph partitioning approach. In: CVPR, pp. 789-796 (2012)
9. Malisiewicz, T., Efros, A.A.: Improving spatial support for objects via multiple segmentations. In: BMVC (2007)
10. Martin, D.R., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: ICCV, pp. 416-425 (2001)
11. Meila, M.: Comparing clusterings: an axiomatic view. In: ICML, pp. 577-584 (2005)
12. Pati, Y., Rezaiifar, R., Krishnaprasad, P.: Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: 27th Asilomar Conference on Signals, Systems and Computers, pp. 40-44 (1993)
13. Shi, J., Malik, J.: Normalized cuts and image segmentation. PAMI 22(8), 888-905 (2000)
14. Shotton, J., Winn, J.M., Rother, C., Criminisi, A.: Textonboost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 1-15. Springer, Heidelberg (2006) (Pubitemid 44029764)
15. Unnikrishnan, R., Pantofaru, C., Hebert, M.: Toward objective evaluation of image segmentation algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 929-944 (2007)
16. Wang, X., Li, H., Masnou, S., Chen, L.: A graph-cut approach to image segmentation using an affinity graph based on -0 - sparse representation of features. In: IEEE Int. Conf. on Image Proc. (2013) (accepted)
17. Yu, Z., Li, A., Au, O., Xu, C.: Bag of textons for image segmentation via soft clustering and convex shift. In: CVPR, pp. 781-788 (2012)
18. Zou, W., Kpalma, K., Ronsin, J.: Semantic segmentation via sparse coding over hierarchical regions. In: ICIP, pp. 2577-2580 (2012)