Automatic segmentation of object region using graph cuts based on saliency maps and AdaBoost

Digest of Technical Papers - IEEE International Conference on Consumer Electronics

Volumn , Issue , 2009, Pages 36-37

  Fukuda, Keita ^a   Takiguchi, Tetsuya ^a   Ariki, Yasuo ^a  

^a KOBE UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ADABOOST; AUTOMATIC SEGMENTATIONS; BAYES' THEOREM; COLOR INFORMATION; CONVENTIONAL METHODS; GAUSSIAN MIXTURE MODEL; GRAPH CUT; IMAGE REGIONS; INTERACTIVE METHODS; LINK COST; OBJECT MODEL; OBJECT REGION; POSTERIOR PROBABILITY; PRIOR PROBABILITY; REGION SEGMENTATION; SALIENCY MAP; SEGMENTATION RESULTS; SEMI-AUTOMATICS; SPATIAL INFORMATIONS; USER INPUT; VISUAL ATTENTION;

ADAPTIVE BOOSTING; CONSUMER ELECTRONICS; PROBABILITY;

IMAGE SEGMENTATION;

EID: 70350224119     PISSN: 0747668X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISCE.2009.5156907     Document Type: Conference Paper

References (5)

1. Y. Boykov and M. P. Jolly, Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images, In Proc. IEEE Conf. on ICCV, pp. 105-112, 2001.
2. T. Nagahashi, H. Fujiyoshi, and T. Kanade, Image segmentation using iterated Graph Cuts based on multi-scale smoothing, In Proc. ACCV, pp. 806-816, 2007.
3. K. Fukuda, T. Takiguchi, and Y. Ariki, Graph cuts by using local texture features of wavelet coefficient for image segmentation, In Proc. IEEE ICME, pp. 881-884, 2008.
4. D. Han, W. Li, X. Lu, T. Wang, and Y. Wang, Automatic segmentation based on AdaBoost learning and Graph-Cuts, In Proc. ICIAR, pp. 215-225, 2006.
5. L. Itti, C. Koch, and E. Niebur, A model of saliency-based visual attention for rapid scene analysis, IEEE Trans. on PAMI, Vol. 20, No. 11, pp. 1254-1259, 1998.