메뉴 건너뛰기




Volumn 34, Issue 2, 2012, Pages 359-371

Recursive segmentation and recognition templates for image parsing

Author keywords

Hierarchy; parsing; scene labeling; segmentation

Indexed keywords

COARSE-TO-FINE; CONTEXTUAL INFORMATION; HIERARCHICAL REPRESENTATION; HIERARCHY; IMAGE DATASETS; IMAGE LABELING; IMAGE MODELS; IMAGE PARSING; INFERENCE ALGORITHM; LABELED DATA; LONG-RANGE DEPENDENCIES; MACHINE LEARNING METHODS; MULTIPLE LEVELS; NATURAL LANGUAGE MODEL; NOUN PHRASE; PARSING; POLYNOMIAL-TIME ALGORITHMS; SENTENCE STRUCTURES; STATE-OF-THE-ART METHODS;

EID: 84055217859     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2011.160     Document Type: Article
Times cited : (39)

References (49)
  • 1
    • 84930565154 scopus 로고
    • Computation of the probability of initial substring generation by stochastic context-free gram mars
    • F. Jelinek and J.D. Lafferty, "Computation of the Probability of Initial Substring Generation by Stochastic Context-Free Gram mars," Computational Linguistics, vol. 17, no. 3, pp. 315-323, 1991.
    • (1991) Computational Linguistics , vol.17 , Issue.3 , pp. 315-323
    • Jelinek, F.1    Lafferty, J.D.2
  • 3
    • 0002532056 scopus 로고
    • The estimation of stochastic context-free grammars using the inside-outside algorithm
    • K. Lari and S.J. Young, "The Estimation of Stochastic Context-Free Grammars Using the Inside-Outside Algorithm," Computer Speech and Language, vol. 4, pp. 35-56, 1990.
    • (1990) Computer Speech and Language , vol.4 , pp. 35-56
    • Lari, K.1    Young, S.J.2
  • 4
    • 33745887513 scopus 로고    scopus 로고
    • Learning non-generative grammatical models for document analysis
    • DOI 10.1109/ICCV.2005.140, 1544825, Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005
    • M. Shilman, P. Liang, and P.A. Viola, "Learning Non-Generative Grammatical Models for Document Analysis," Proc. IEEE Int'l Conf. Computer Vision, pp. 962-969, 2005. (Pubitemid 44042285)
    • (2005) Proceedings of the IEEE International Conference on Computer Vision , vol.II , pp. 962-969
    • Shilman, M.1    Liang, P.2    Viola, P.3
  • 5
    • 0036566199 scopus 로고    scopus 로고
    • Image segmentation by data-driven Markov Chain Monte Carlo
    • DOI 10.1109/34.1000239
    • Z. Tu and S.C. Zhu, "Image Segmentation by Data-Driven Markov Chain Monte Carlo," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 657-673, May 2002. (Pubitemid 34550432)
    • (2002) IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.24 , Issue.5 , pp. 657-673
    • Tu, Z.1    Zhu, S.-C.2
  • 9
    • 0028392841 scopus 로고
    • A multiscale random field model for bayesian image segmentation
    • Mar.
    • C. Bouman and M. Shapiro, "A Multiscale Random Field Model for Bayesian Image Segmentation," IEEE Trans. Image Processing, vol. 3, no. 2, pp. 162-177, Mar. 1994.
    • (1994) IEEE Trans. Image Processing , vol.3 , Issue.2 , pp. 162-177
    • Bouman, C.1    Shapiro, M.2
  • 10
    • 33747727601 scopus 로고    scopus 로고
    • Wavelet-based statistical signal processing using hidden Markov models
    • PII S1053587X98025045
    • M.S. Crouse, R.D. Nowak, and R.G. Baraniuk, "Wavelet-Based Statistical Signal Processing Using Hidden Markov Models," IEEE Trans. Signal Processing, vol. 46, no. 4, pp. 886-902, Apr. 1998. (Pubitemid 128743828)
    • (1998) IEEE Transactions on Signal Processing , vol.46 , Issue.4 , pp. 886-902
    • Crouse, M.S.1    Nowak, R.D.2    Baraniuk, R.G.3
  • 11
    • 31144461915 scopus 로고    scopus 로고
    • Varying complexity in tree-structured image distribution models
    • DOI 10.1109/TIP.2005.860601
    • C. Spence, L.C. Parra, and P. Sajda, "Varying Complexity in Tree-Structured Image Distribution Models," IEEE Trans. Image Proces sing, vol. 15, no. 2, pp. 319-330, Feb. 2006. (Pubitemid 43126157)
    • (2006) IEEE Transactions on Image Processing , vol.15 , Issue.2 , pp. 319-330
    • Spence, C.1    Parra, L.C.2    Sajda, P.3
  • 13
    • 0345421566 scopus 로고    scopus 로고
    • Multiresolution Markov models for signal and image processing
    • DOI 10.1109/JPROC.2002.800717, PII 1011092002800717
    • A.S. Willsky, "Multiresolution Markov Models for Signal and Image Processing," Proc. IEEE, vol. 90, no. 8, pp. 1396-1458, Aug. 2002. (Pubitemid 43785865)
    • (2002) Proceedings of the IEEE , vol.90 , Issue.8 , pp. 1396-1458
    • Willsky, A.S.1
  • 14
    • 0142192295 scopus 로고    scopus 로고
    • Conditional random fields: Probabilistic models for segmenting and labeling sequence data
    • J.D. Lafferty, A. McCallum, and F.C.N. Pereira, "Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data," Proc. 18th Int'l Conf. Machine Learning, pp. 282-289, 2001.
    • (2001) Proc. 18th Int'l Conf. Machine Learning , pp. 282-289
    • Lafferty, J.D.1    McCallum, A.2    Pereira, F.C.N.3
  • 17
    • 85127836544 scopus 로고    scopus 로고
    • Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms
    • M. Collins, "Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms," Proc. ACL-02 Conf. Empirical Methods in Natural Language Processing, pp. 1-8, 2002.
    • (2002) Proc. ACL-02 Conf. Empirical Methods in Natural Language Processing , pp. 1-8
    • Collins, M.1
  • 20
    • 0021518209 scopus 로고
    • Stochastic relaxation, gibbs distributions, and the bayesian restoration of images
    • S. Geman and D. Geman, "Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 6, no. 6, pp. 721-741, Nov. 1984. (Pubitemid 15453722)
    • (1984) IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PAMI-6 , Issue.6 , pp. 721-741
    • Geman Stuart1    Geman Donald2
  • 22
    • 84990602490 scopus 로고
    • Optimal approximations of piecewise smooth functions and associated variational problems
    • D. Mumford and J. Shah, "Optimal Approximations of Piecewise Smooth Functions and Associated Variational Problems," Comm. Pure and Applied Math., vol. 42, pp. 577-685, 1989.
    • (1989) Comm. Pure and Applied Math. , vol.42 , pp. 577-685
    • Mumford, D.1    Shah, J.2
  • 23
    • 0026151642 scopus 로고
    • Parallel and deterministic algorithms from MRF's: Surface reconstruction
    • DOI 10.1109/34.134040
    • D. Geiger and F. Girosi, "Parallel and Deterministic Algorithms from Mrfs: Surface Reconstruction," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 5, pp. 401-412, May 1991. (Pubitemid 21676528)
    • (1991) IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.13 , Issue.5 , pp. 401-412
    • Geiger Davi1    Girosi Federico2
  • 24
    • 0026201666 scopus 로고
    • A common framework for image segmentation
    • D. Geiger and A. Yuille, "A Common Framework for Image Segmentation," Int'l J. Computer Vision, vol. 6, pp. 227-243, 1991.
    • (1991) Int'l J. Computer Vision , vol.6 , pp. 227-243
    • Geiger, D.1    Yuille, A.2
  • 26
    • 0000806445 scopus 로고    scopus 로고
    • Minimax entropy principle and its application to texture modeling
    • S.C. Zhu, Y.N. Wu, and D. Mumford, "Minimax Entropy Principle and Its Application to Texture Modeling," Neural Computation, vol. 9, no. 8, pp. 1627-1660, 1997. (Pubitemid 127462299)
    • (1997) Neural Computation , vol.9 , Issue.8 , pp. 1627-1660
    • Zhu, S.C.1    Wu, Y.N.2    Mumford, D.3
  • 30
    • 0344120654 scopus 로고    scopus 로고
    • Discriminative random fields: A discriminative framework for contextual interaction in classification
    • S. Kumar and M. Hebert, "Discriminative Random Fields: A Discriminative Framework for Contextual Interaction in Classification," Proc. Ninth IEEE Int'l Conf. Computer Vision, 2003.
    • (2003) Proc. Ninth IEEE Int'l Conf. Computer Vision
    • Kumar, S.1    Hebert, M.2
  • 31
    • 33745848658 scopus 로고    scopus 로고
    • A hierarchical field framework for unified context-based classification
    • DOI 10.1109/ICCV.2005.9, 1544868, Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005
    • S. Kumar and M. Hebert, "A Hierarchical Field Framework for Unified Context-Based Classification," Proc. 10th IEEE Int'l Conf. Computer Vision, pp. 1284-1291, 2005. (Pubitemid 44042328)
    • (2005) Proceedings of the IEEE International Conference on Computer Vision , vol.II , pp. 1284-1291
    • Kumar, S.1    Hebert, M.2
  • 34
    • 0030247213 scopus 로고    scopus 로고
    • Region competition: Unifying snakes, region growing, and bayes/mdl for multiband image segmentation
    • S. Zhu and A. Yuille, "Region Competition: Unifying Snake/Balloon, Region Growing and Bayes/MDL/Energy for Multi-Band Image Segmentation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 884-900, Sept. 1996. (Pubitemid 126772831)
    • (1996) IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.18 , Issue.9 , pp. 884-900
    • Zhu, S.C.1
  • 39
    • 24044435942 scopus 로고    scopus 로고
    • Reducing multiclass to binary: A unifying approach for margin classifiers
    • E.L. Allwein, R.E. Schapire, and Y. Singer, "Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers," J. Machine Learning Research, vol. 1, pp. 113-141, 2000. (Pubitemid 33738778)
    • (2001) Journal of Machine Learning Research , vol.1 , Issue.2 , pp. 113-141
    • Allwein, E.L.1    Schapire, R.E.2    Singer, Y.3
  • 41
    • 0042565834 scopus 로고    scopus 로고
    • Hierarchical bayesian inference in the visual cortex
    • T. Lee and D. Mumford, "Hierarchical Bayesian Inference in the Visual Cortex," J. Optical Soc. Am., vol. 20, pp. 1434-1448, 2003.
    • (2003) J. Optical Soc. Am. , vol.20 , pp. 1434-1448
    • Lee, T.1    Mumford, D.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.