Cited by 397Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval TechniquesBayesian Modeling and Causal Inference

The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.

Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering

Zhuowen Tu·2005·Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1

DOI 10.1109/iccv.2005.194

Open this paper in CoChat — free account

Abstract

In this paper, a new learning framework - probabilistic boosting-tree (PBT), is proposed for learning two-class and multi-class discriminative models. In the learning stage, the probabilistic boosting-tree automatically constructs a tree in which each node combines a number of weak classifiers (evidence, knowledge,) into a strong classifier (a conditional posterior probability). It approaches the target posterior distribution by data augmentation (tree expansion) through a divide-and-conquer strategy. In the testing stage, the conditional probability is computed at each tree node based on the learned classifier, which guides the probability propagation in its sub-trees. The top node of the tree therefore outputs the overall posterior probability by integrating the probabilities gathered from its sub-trees. Also, clustering is naturally embedded in the learning phase and each sub-tree represents a cluster of certain level. The proposed framework is very general and it has interesting connections to a number of existing methods such as the A* algorithm, decision tree algorithms, generative models, and cascade approaches. In this paper, we show the applications of PBT for classification, detection, and object recognition. We have also applied the framework in segmentation

Integrity signals

Registry

Crossref · 10.1109/iccv.2005.194

Cites

23 of 23 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors)
Friedman, J., Hastie, T., & Tibshirani, R. (2000) · The Annals of Statistics · doi:10.1214/aos/1016218223 · Cited by 6,884
Neural network-based face detection
Rowley, H. A., Baluja, S., & Kanade, T. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.655647 · Cited by 3,554
Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories
Fei-Fei, L., Fergus, R., & Perona, P. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2005.09.012 · Cited by 2,359
Object class recognition by unsupervised scale-invariant learning
Fergus, R., Perona, P., & Zisserman, A. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2003.1211479 · Cited by 2,030
Learning methods for generic object recognition with invariance to pose and lighting
LeCun, Y., Huang, F. J., & Bottou, L. (2004) · Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. · doi:10.1109/cvpr.2004.1315150 · Cited by 1,324
Shape Matching and Object Recognition Using Low Distortion Correspondences
Berg, A. C., Berg, T. L., & Malik, J. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2005.320 · Cited by 844
Analyzing appearance and contour based methods for object categorization
Leibe, B., & Schiele, B. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2003.1211497 · Cited by 798
Sharing Visual Features for Multiclass and Multiview Object Detection
Torralba, A. B., Murphy, K. P., & Freeman, W. T. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1055 · Cited by 704
Fast Multi-view Face Detection
Jones, M., & Viola, P. A. (2003) · CTIT technical reports series · Cited by 615

Show all 23 references

Research works printed but not linked: 1

friedman, 1998, Additive logistic regression: A statistical view of boosting, Stanford Univ Tech Rep

Sorted by citations

Contour Detection and Hierarchical Image Segmentation
Arbeláez, P., Maire, M., Fowlkes, C. C., & Malik, J. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.161 · Cited by 5,581
Integral Channel Features
Dollar, P., Tu, Z., Perona, P., & Belongie, S. (2009) · Procedings of the British Machine Vision Conference 2009 · doi:10.5244/c.23.91 · Cited by 1,237
Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning
Criminisi, A. (2011) · now publishers, Inc. eBooks · doi:10.1561/9781601985415 · Cited by 723
AI in Medical Imaging Informatics: Current Challenges and Future Directions
Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., Kurc, T., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2991043 · Cited by 713
Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation
Tu, Z., & Bai, X. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.186 · Cited by 578
DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection
Shen, W., Wang, X., Wang, Y., Bai, X., & Zhang, Z. (2015) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2015.7299024 · Cited by 554
Four-Chamber Heart Modeling and Automatic Segmentation for 3-D Cardiac CT Volumes Using Marginal Space Learning and Steerable Features
Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., & Comaniciu, D. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.2004421 · Cited by 546
A Survey of Recent Advances in Face Detection
Zhang, C., & Zhang, Z. (2010) · Cited by 511
A survey on face detection in the wild: Past, present and future
Zafeiriou, S. P., Zhang, C., & Zhang, Z. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.03.015 · Cited by 476
Supervised Learning of Edges and Object Boundaries
Dollár, P., Zhuowen Tu, A., & Belongie, S. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.298 · Cited by 428
Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review
Lemaître, G., Martí, R., Freixenet, J., Vilanova, J. C., WALKER, P. M., & Mériaudeau, F. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.02.009 · Cited by 363
Multi-Scale Deep Reinforcement Learning for Real-Time 3D-Landmark Detection in CT Scans
Ghesu, F.-C., Georgescu, B., Zheng, Y., Grbic, S., Maier, A., Hornegger, J., & Comaniciu, D. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2782687 · Cited by 310
Recent advances on image edge detection: A comprehensive review
Jing, J., Liu, S., Wang, G., Zhang, W., & Sun, C. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.06.083 · Cited by 328
Combining randomization and discrimination for fine-grained image categorization
Yao, B., Khosla, A., & Fei-Fei, L. (2011) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2011.5995368 · Cited by 291
Automatic breast ultrasound image segmentation: a survey
Xian, M., Zhang, Y., Cheng, H. D., Xu, F., Zhang, B., & Ding, J. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.02.012 · Cited by 284
“In [142], the contour with max ARD value [25,26] was chosen as the final tumor contour.”
Method
Vision-based and marker-less surgical tool detection and tracking: a review of the literature
Bouget, D., Allan, M., Stoyanov, D. V., & Jannin, P. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.09.003 · Cited by 292
Auto-context and its application to high-level vision tasks
Zhuowen Tu, A. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587436 · Cited by 239
Comparison of AdaBoost and Support Vector Machines for Detecting Alzheimer's Disease Through Automated Hippocampal Segmentation
Morra, J. H., Tu, Z., Apostolova, L. G., Green, A. E., Toga, A. W., & Thompson, P. M. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2021941 · Cited by 238
On Oblique Random Forests
Menze, B. H., Kelm, B. M., Splitthoff, D. N., Köthe, U., & Hamprecht, F. A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23783-6_29 · Cited by 234
GLISTR: Glioma Image Segmentation and Registration
Gooya, A., Pohl, K. M., Bilello, M., Cirillo, L., Biros, G., Melhem, E. R., & Davatzikos, C. A. (2012) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2012.2210558 · Cited by 217
AnL1image transform for edge-preserving smoothing and scene-level intrinsic decomposition
Bi, S., Han, X., & Yu, Y. (2015) · ACM Transactions on Graphics · doi:10.1145/2766946 · Cited by 215
Detection and Measurement of Fetal Anatomies from Ultrasound Images using a Constrained Probabilistic Boosting Tree
Carneiro, G., Georgescu, B., Good, S., & Comaniciu, D. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.928917 · Cited by 205
Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning
Criminisi, A., Ender Konukoglu, A., & Shotton, J. (2011) · Cited by 204
Patient-Specific Modeling and Quantification of the Aortic and Mitral Valves From 4-D Cardiac CT and TEE
Ionasec, R. I., Voigt, I., Georgescu, B., Wang, Y., Houle, H. C., Vega-Higuera, F., Navab, N., & Comaniciu, D. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2048756 · Cited by 192
Cluster Boosted Tree Classifier for Multi-View, Multi-Pose Object Detection
Wu, B., & Nevatia, R. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4409006 · Cited by 189
Brain Anatomical Structure Segmentation by Hybrid Discriminative/Generative Models
Tu, Z., Narr, K. L., Dollár, P., Dinov, I. D., Thompson, P. M., & Toga, A. W. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2007.908121 · Cited by 187
Ultrasound image segmentation and tissue characterization
Noble, J. A. (2009) · Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · doi:10.1243/09544119jeim604 · Cited by 159
On the Design of Cascades of Boosted Ensembles for Face Detection
Brubaker, S. C., Wu, J., Sun, J., Mullin, M. D., & Rehg, J. M. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-007-0060-1 · Cited by 155
3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data
Zheng, Y., Liu, D., Georgescu, B., Nguyen, H. V., & Comaniciu, D. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24553-9_69 · Cited by 151
Marginal space deep learning: efficient architecture for volumetric image parsing
Ghesu, F. C., Krubasik, E., Georgescu, B., Singh, V., Zheng, Y., Hornegger, J., & Comaniciu, D. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2538802 · Cited by 142
Detecting People Looking at Each Other in Videos
Marín-Jiménez, M., Zisserman, A., Eichner, M., & Ferrari, V. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0655-7 · Cited by 163
Hierarchical, learning-based automatic liver segmentation
Ling, H., Zhou, S., Zheng, Y., Georgescu, B., Suehling, M., & Comaniciu, D. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587393 · Cited by 140
Hierarchical parsing and semantic navigation of full body CT data
Seifert, S., Barbu, A., Zhou, S. K., Liu, D., Johannes Feulner, A., Huber, M., Suehling, M., Cavallaro, A., & Comaniciu, D. (2009) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.812214 · Cited by 137
Fast Automatic Heart Chamber Segmentation from 3D CT Data Using Marginal Space Learning and Steerable Features
Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., & Comaniciu, D. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408925 · Cited by 134
A stochastic graph grammar for compositional object representation and recognition
Lin, L., Wu, T., Porway, J., & Xu, Z. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2008.10.033 · Cited by 130
Neural Decision Forests for Semantic Image Labelling
Bulò, S. R., & Kontschieder, P. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.18 · Cited by 133
Entangled Decision Forests and Their Application for Semantic Segmentation of CT Images
Montillo, A., Shotton, J., Winn, J. M., Iglesias, J. E., Metaxas, D., & Criminisi, A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22092-0_16 · Cited by 126
Patient-specific modelling of whole heart anatomy, dynamics and haemodynamics from four-dimensional cardiac CT images
Mihalef, V., Ionasec, R. I., Sharma, P. K., Georgescu, B., Voigt, I., Suehling, M., & Comaniciu, D. (2011) · Interface Focus · doi:10.1098/rsfs.2010.0036 · Cited by 123
Longitudinal clinical score prediction in Alzheimer's disease with soft-split sparse regression based random forest
Huang, L., Jin, Y., Gao, Y., Thung, K., & Shen, D. G. (2016) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2016.07.005 · Cited by 120
Textural Kinetics: A Novel Dynamic Contrast-Enhanced (DCE)-MRI Feature for Breast Lesion Classification
Agner, S. C., Soman, S., Libfeld, E., McDonald, M., Thomas, K., Englander, S. A., Rosen, M. A., Chin, D., Nosher, J. L., & Madabhushi, A. (2010) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-010-9298-1 · Cited by 118
Multiple-Instance Pruning For Learning Efficient Cascade Detectors
Zhang, C., & Viola, P. A. (2007) · Cited by 116
Long-term performance prediction framework based on XGBoost decision tree for pultruded FRP composites exposed to water, humidity and alkaline solution
Liu, X., Liu, T., & Feng, P. (2022) · Composite Structures · doi:10.1016/j.compstruct.2022.115184 · Cited by 109
Cascaded discrimination of normal, abnormal, and confounder classes in histopathology: Gleason grading of prostate cancer
Doyle, S. T., Feldman, M. D., Shih, N. N. C., Tomaszewski, J., & Madabhushi, A. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-282 · Cited by 105
Evaluating Segmentation Error without Ground Truth
Kohlberger, T., Singh, V., Alvino, C., Bahlmann, C., & Grady, L. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33415-3_65 · Cited by 102
Building more accurate decision trees with the additive tree
Luna, J. M., Gennatas, E. D., Ungar, L., Eaton, E., Diffenderfer, E. S., Jensen, S. T., Simone, C. B., Friedman, J. H., Solberg, T. D., & Valdés, G. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1816748116 · Cited by 101
Region-Based Discriminative Feature Pooling for Scene Text Recognition
Lee, C., Bhardwaj, A., Di, W., Jagadeesh, V., & Piramuthu, R. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.516 · Cited by 99
BUSIS: A Benchmark for Breast Ultrasound Image Segmentation
Zhang, Y., Xian, M., Cheng, H.-D., Shareef, B., Ding, J., Xu, F., Huang, K., Zhang, B., Ning, C., & Wang, Y. (2022) · Healthcare · doi:10.3390/healthcare10040729 · Cited by 107
Learning an Alphabet of Shape and Appearance for Multi-Class Object Detection
Opelt, A., Pinz, A. J., & Zisserman, A. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-008-0139-3 · Cited by 95
Data Mining and Knowledge Discovery in Real Life Applications
Ponce, J., & Karahoca, A. (2009) · doi:10.5772/97 · Cited by 96
Face Gender Classification on Consumer Images in a Multiethnic Environment
Gao, W., & Ai, H. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-01793-3_18 · Cited by 94
Conditional Convolutional Neural Network for Modality-Aware Face Recognition
Xiong, C., Zhao, X., Tang, D., Jayashree, K., Yan, S., & Kim, T. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.418 · Cited by 93
A Statistical Model for Quantification and Prediction of Cardiac Remodelling: Application to Tetralogy of Fallot
Mansi, T., Voigt, I., Leonardi, B., Xavier Pennec, B., Durrleman, S., Maxime Sermesant, B., Delingette, H., Taylor, A. M., Boudjemline, Y., Pongiglione, G., et al. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2135375 · Cited by 88
Simultaneous Learning and Alignment: Multi-Instance and Multi-Pose Learning ?
Babenko, B., Tu, Z., & Belongie, S. (2008) · Cited by 86
Object Categorization
Pinz, A. J. (2005) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000003 · Cited by 84
Pavement Distress Detection Using Random Decision Forests
Cui, L., Qi, Z., Chen, Z., Meng, F., & Shi, Y. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24474-7_14 · Cited by 85
Encoding atlases by randomized classification forests for efficient multi-atlas label propagation
Zikic, D., Glocker, B., & Criminisi, A. (2014) · Medical Image Analysis · doi:10.1016/j.media.2014.06.010 · Cited by 81
A Discriminative Model-Constrained Graph Cuts Approach to Fully Automated Pediatric Brain Tumor Segmentation in 3-D MRI
Wels, M., Carneiro, G., Aplas, A., Huber, M., Joachim Hornegger, A., & Comaniciu, D. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85988-8_9 · Cited by 80
Lymph node detection and segmentation in chest CT data using discriminative learning and a spatial prior
Feulner, J., Kevin Zhou, S., Hammon, M., Hornegger, J., & Comaniciu, D. (2013) · Medical Image Analysis · doi:10.1016/j.media.2012.11.001 · Cited by 72
HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition
Jianping Fan, B., Zhao, T., Kuang, Z., Zheng, Y., Zhang, J., Yu, J., & Peng, J. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2667405 · Cited by 76
Multimodal wavelet embedding representation for data combination (MaWERiC): integrating magnetic resonance imaging and spectroscopy for prostate cancer detection
Tiwari, P., Viswanath, S. E., Kurhanewicz, J. V., Sridhar, A., & Madabhushi, A. (2011) · NMR in Biomedicine · doi:10.1002/nbm.1777 · Cited by 71
An active learning based classification strategy for the minority class problem: application to histopathology annotation
Doyle, S. T., Monaco, J., Feldman, M., Tomaszewski, J., & Madabhushi, A. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-424 · Cited by 69
Automatic fetal biometry prediction using a novel deep convolutional network architecture
Oghli, M. G., Shabanzadeh, A., Moradi, S., Sirjani, N., Gerami, R., Ghaderi, P., Taheri, M. S., Shiri, I., Arabi, H., & Zaidi, H. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.06.020 · Cited by 70
Robust guidewire tracking in fluoroscopy
Wang, P., Chen, T., Zhu, Y., Zhang, W., Zhou, S. K., & Comaniciu, D. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206692 · Cited by 72
Multi-organ localization with cascaded global-to-local regression and shape prior
Gauriau, R., Cuingnet, R., Lesage, D., & Isabelle Bloch, A. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.04.007 · Cited by 67
Complete valvular heart apparatus model from 4D cardiac CT
Grbić, S., Ionasec, R. I., Vitanovski, D., Voigt, I., Wang, Y., Georgescu, B., Navab, N., & Comaniciu, D. (2012) · Medical Image Analysis · doi:10.1016/j.media.2012.02.003 · Cited by 66
Hierarchical segmentation and identification of thoracic vertebra using learning-based edge detection and coarse-to-fine deformable model
Jun, M., & Lü, L. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2012.11.016 · Cited by 66
3D ultrasound tracking of the left ventricle using one-step forward prediction and data fusion of collaborative trackers
Yang, L., Georgescu, B., Zheng, Y., Meer, P., & Comaniciu, D. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587518 · Cited by 65
Image spam hunter
Gao, Y., Yang, M., Zhao, X., Pardo, B., Wu, Y. N., Pappas, T. N., & Choudhary, A. (2008) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2008.4517972 · Cited by 66
Rapid Multi-organ Segmentation Using Context Integration and Discriminative Models
Lay, N. S., Birkbeck, N., Zhang, J., & Zhou, S. K. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38868-2_38 · Cited by 66
Automatic Cell Detection in Bright-Field Microscope Images Using SIFT, Random Forests, and Hierarchical Clustering
Mualla, F., Schöll, S., Sommerfeldt, B., Maier, A., & Joachim Hornegger, A. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2280380 · Cited by 65
Automated Extraction of the Cortical Sulci Based on a Supervised Learning Approach
Zhuowen Tu, A., Zheng, S., Yuille, A., Reiss, A. L., Dutton, R. A., Lee, A. D., Galaburda, A. M., Dinov, I. D., Thompson, P. M., & Toga, A. W. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2007.892506 · Cited by 65
Detection and recognition of contour parts based on shape similarity
Bai, X., Yang, X., & Latecki, L. J. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2007.12.016 · Cited by 63
Prediction Based Collaborative Trackers (PCT): A Robust and Accurate Approach Toward 3D Medical Object Tracking
Yang, L., Georgescu, B., Zheng, Y., Wang, Y., Meer, P., & Comaniciu, D. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2158440 · Cited by 59
Automatic View Planning for Cardiac MRI Acquisition
Lu, X., Jolly, M., Georgescu, B., Hayes, C. M. G., Speier, P., Schmidt, M., Bi, X., Kroeker, R. M., Comaniciu, D., Kellman, P., et al. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23626-6_59 · Cited by 58
Multi-stage Learning for Robust Lung Segmentation in Challenging CT Volumes
Sofka, M., Wetzl, J., Birkbeck, N., Zhang, J., Kohlberger, T., Kaftan, J. N., Declerck, J. M., & Zhou, S. K. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23626-6_82 · Cited by 58
Automatic Multi-organ Segmentation Using Learning-Based Segmentation and Level Set Optimization
Kohlberger, T., Sofka, M., Zhang, J., Birkbeck, N., Wetzl, J., Kaftan, J. N., Declerck, J. M., & Zhou, S. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23626-6_42 · Cited by 58
Probabilistic 3D Polyp Detection in CT Images: The Role of Sample Alignment
Tu, Z., Zhou, X. S., Bogoni, L., Barbu, A., & Comaniciu, D. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.228 · Cited by 59
Automatic Cardiac View Classification of Echocardiogram
PARK, J. H., Zhou, S. K., Simopoulos, C., Otsuki, J., & Comaniciu, D. (2007) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2007.4408867 · Cited by 56
"Here's looking at you, kid". Detecting people looking at each other in videos
Marín-Jiménez, M. J., Zisserman, A., & Ferrari, V. (2011) · British Machine Vision Conference (BMVC) · doi:10.5244/c.25.22 · Cited by 55
Hierarchical Learning of Curves Application to Guidewire Localization in Fluoroscopy
Barbu, A., Athitsos, V., Georgescu, B., Boehm, S., Durlak, P., & Comaniciu, D. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383033 · Cited by 54
A novel clustering-based image segmentation via density peaks algorithm with mid-level feature
Shi, Y., Chen, Z., Qi, Z., Meng, F., & Cui, L. (2016) · Neural Computing and Applications · doi:10.1007/s00521-016-2300-1 · Cited by 54
Intelligent scanning: Automated standard plane selection and biometric measurement of early gestational sac in routine ultrasound examination
Zhang, L., Chen, S., Chin, C. T., Wang, T., & Li, S. (2012) · Medical Physics · doi:10.1118/1.4736415 · Cited by 53
Less is More: Simultaneous View Classification and Landmark Detection for Abdominal Ultrasound Images
Xu, Z., Huo, Y., Park, J., Landman, B. A., Milkowski, A., Grbić, S., & Zhou, S. K. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00934-2_79 · Cited by 53
Shape regression machine and efficient segmentation of left ventricle endocardium from 2D B-mode echocardiogram
Zhou, S. K. (2010) · Medical Image Analysis · doi:10.1016/j.media.2010.04.002 · Cited by 52
Image-Based Multiclass Boosting and Echocardiographic View Classification
Zhou, S. K., PARK, J. H., Georgescu, B., Comaniciu, D., Simopoulos, C., & Otsuki, J. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.146 · Cited by 51
Discriminative Sub-categorization
Hoai, M., & Zisserman, A. (2013) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2013.218 · Cited by 50
Interactive Image Segmentation Based on Level Sets of Probabilities
Liu, Y., & Yu, Y. (2011) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2011.77 · Cited by 50
Detecting Object Boundaries Using Low-, Mid-, and High-level Information
Zheng, S., Zhuowen Tu, A., & Yuille, A. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383343 · Cited by 50
Machine learning based vesselness measurement for coronary artery segmentation in cardiac CT volumes
Zheng, Y., Loziczonek, M., Georgescu, B., Zhou, S. K., Vega-Higuera, F., & Comaniciu, D. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.877233 · Cited by 48
Representing and recognizing objects with massive local image patches
Lin, L., Luo, P., Chen, X., & Zeng, K. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.06.011 · Cited by 46
Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study
Viswanath, S. E., Chirra, P. V., Yim, M. C., Rofsky, N. M., Purysko, A. S., Rosen, M. A., Bloch, B., & Madabhushi, A. (2019) · BMC Medical Imaging · doi:10.1186/s12880-019-0308-6 · Cited by 46
Segment 2D and 3D Filaments by Learning Structured and Contextual Features
Gu, L., Zhang, X., Zhao, H., Li, H., & Cheng, L. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2623357 · Cited by 43
Joint Real-time Object Detection and Pose Estimation Using Probabilistic Boosting Network
Zhang, J., Zhou, S. K., McMillan, L., & Comaniciu, D. (2007) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2007.383275 · Cited by 43
Learning without Labeling: Domain Adaptation for Ultrasound Transducer Localization
Heimann, T., Mountney, P., John, M., & Ionasec, R. I. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40760-4_7 · Cited by 42
A learning based deformable template matching method for automatic rib centerline extraction and labeling in CT images
Wu, D., Liu, D., Puskas, Z., Lu, C., Wimmer, A., Tietjen, C., Soza, G., & Zhou, S. K. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247774 · Cited by 41
Learning Class-Specific Edges for Object Detection and Segmentation
Prasad, M., Zisserman, A., Fitzgibbon, A. W., Kumar, M. P. P., & Torr, P. H. S. (2006) · Lecture notes in computer science · doi:10.1007/11949619_9 · Cited by 41
Detecting object boundaries using low-, mid-, and high-level information
Zheng, S., Yuille, A., & Zhuowen Tu, A. (2010) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2010.07.004 · Cited by 41
Automatic Detection and Measurement of Structures in Fetal Head Ultrasound Volumes Using Sequential Estimation and Integrated Detection Network (IDN)
Sofka, M., Zhang, J., Good, S., Zhou, S. K., & Comaniciu, D. (2014) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2301936 · Cited by 40
A Comprehensive Approach to Image Spam Detection: From Server to Client Solution
Gao, Y., Choudhary, A., & Hua, G. (2010) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2010.2080267 · Cited by 41
Automatic Detection and Segmentation of Focal Liver Lesions in Contrast Enhanced CT Images
Militzer, A., Hager, T., Jager, F., Tietjen, C., & Joachim Hornegger, A. (2010) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2010.618 · Cited by 40

Authors: Tu, Z.
Venue: Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1
Publisher: IEEE
Published: 2005
Pages: 1589-1596 Vol. 2
DOI: 10.1109/iccv.2005.194
OpenAlex: W2146514558
Cited by: 397
References: 23
Citations by year: 2026: 1 · 2025: 1 · 2024: 3 · 2023: 1 · 2022: 6 · 2021: 5 · 2020: 4 · 2019: 13 · 2018: 8 · 2017: 15 · 2016: 16 · 2015: 27 · 2014: 33 · 2013: 31 · 2012: 33
FWCI: 16.11
Type: conference paper
Language: en
Open access: closed
Locations: IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
Keywords: discriminative models, weak classifiers, data augmentation, divide-and-conquer, probability propagation, multi-class classification, clustering, object recognition, object detection, image segmentation

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Citations

Cites

Cited by

Related

Metadata

Record

About these checks