Paper published in a book (Scientific congresses and symposiums)
Fast Multi-Class Image Annotation with Random Subwindows and Multiple Output Randomized Trees
Dumont, Marie; Marée, Raphaël ; Wehenkel, Louis et al. 2009 • In Proc. International Conference on Computer Vision Theory and Applications (VISAPP) Peer reviewed
[en] This paper addresses image annotation, i.e. labelling pixels of an image with a class among a finite set of predefined classes. We propose a new method which extracts a sample of subwindows from a set of annotated images in order to train a subwindow annotation model by using the extremely randomized trees ensemble method appropriately extended to handle high-dimensional output spaces. The annotation of a pixel of an unseen image is done by aggregating the annotations of its subwindows containing this pixel. The proposed method is compared to a more basic approach predicting the class of a pixel from a single window centered on that pixel and to other state-of-the-art image annotation methods. In terms of accuracy, the proposed method significantly outperforms the basic method and shows good performances with respect to the state-of-the-art, while being more generic, conceptually simpler, and of higher computational efficiency than these latter.
Disciplines :
Computer science
Author, co-author :
Dumont, Marie; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst. Montefiore) > Systèmes et Modélisation
F.R.S.-FNRS - Fonds de la Recherche Scientifique FEDER - Fonds Européen de Développement Régional BELSPO - SPP Politique scientifique - Service Public Fédéral de Programmation Politique scientifique
Amit, Y., Geman, D., Shape Quantization and Recognition with Randomized Trees (1997) Neural Computation, 9 (7), pp. 1545-1588
Bertelli, L., Byun, J., Manjunath, B.S., A variational approach to exploit prior information in object-background segregation: Application to retinal images (2007) ICIP
Borenstein, E., Malik, J., Shape guided object segmentation (2006) Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006, 1, pp. 969-976. , DOI 10.1109/CVPR.2006.276, 1640856, Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
Bosch, A., Zisserman, A., Munoz, X., Image classification using random forests and ferns (2007) Proc. 'ICCV
Breiman, L., Friedman, J., Olshen, R., Stone, C., Classification and regression trees (1984) Wadsworth and Brooks, , Monterey, CA
Cour, T., Shi, J., Recognizing objects by piecing together the segmentation puzzle (2007) CVPR
Dollar, P., Tu, Z., Belongie, S., Supervised learning of edges and object boundaries (2006) Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006, 2, pp. 1964-1971. , DOI 10.1109/CVPR.2006.298, 1640993, Proceedings - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006
Dumont, M., Marée, R., Wehenkel, L., Geurts, P., (2009), http://www.montefiore.ulg.ac.be/services/stochastic/pubs/2009/DMWGO9/
Ernst, D., Maree, R., Wehenkel, L., Reinforcement learning with raw image pixels as input state (2006) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 446-454. , Advances in Machine Vision, Image Processing, and Pattern Analysis - International Workshop on Intelligent Computing in Pattern Analysis/Synthesis, IWICPAS 2006, Proceedings
Geurts, P., Ernst, D., Wehenkel, L., Extremely randomized trees (2006) Machine Learning, 36 (1), pp. 3-42
Geurts, P., Wehenkel, L., D'Alche-Buc, F., Kernelizing the output of tree-based methods (2006) ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning, 2006, pp. 345-352. , ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning
He, X., Zemel, R., Carreira-Perpinan, M., Multiscale conditional random fields for image labelling (2004) CVPR, 2, pp. 695-702
He, X., Zemel, R.S., Ray, D., Learning and incorporating top-down cues in image segmentation (2006) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 338-351. , Computer Vision - ECCV 2006, 9th European Conference on Computer Vision, Proceedings
Jodogne, S., Briquet, C., Piater, J., Approximate policy iteration for closed-loop learning of visual tasks (2006) Proc. of the 17th European Conference on Machine Learning, 4212, pp. 222-226
Lepetit, V., Fua, P., Keypoint recognition using randomized trees (2006) IEEE Transactions on Pattern Analysis and Machine Intelligence, 28 (9), pp. 1465-1479. , DOI 10.1109/TPAMI.2006.188
Maree, R., Geurts, P., Piater, J., Wehenkel, L., Random subwindows for robust image classification (2005) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, pp. 34-40. , Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005
Marée, R., Geurts, P., Visimberga, G., Piater, J., Wehenkel, L., An empirical comparison of machine learning algorithms for generic image classification (2003) Proc. SGA I-Al, pp. 169-182
Marée, R., Geurts, P., Wehenkel, L., Content-based image retrieval by indexing random subwindows with randomized trees (2007) Proc, 4844, pp. 611-620. , A CCV of LNCS
Meurie, C., Lebrun, G., Lezoray, O., Elmoataz, A., A comparison of supervised pixels-based color image segmentation methods, application in cancerology (2003) WSEAS Transactions on Computers, Special Issue on SSIP '03, 2, pp. 739-744
Moosmann, F., Triggs, B., June, F., Randomized clustering forests for building fast and discriminative visual vocabularies (2006) Neural Information Processing Systems (NIPS)
Nowak, E., Jurie, F., Learning visual similarity measures for comparing never seen objects (2007) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p. 4269994. , DOI 10.1109/CVPR.2007.382969, 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
Nowak, E., Jurie, F., Triggs, B., Sampling strategies for bag-of-features image classification (2006) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 490-503. , DOI 10.1007/11744085-38, Computer Vision - ECCV 2006, 9th European Conference on Computer Vision, Proceedings
Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A., Object retrieval with large vocabularies and fast spatial matching (2007) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p. 4270197. , DOI 10.1109/CVPR.2007.383172, 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
Schroff, F., Criminisi, A., Zisserman, A., Object class segmentation using random forests (2008) Proc. of the British Machine Vision Conference (BMVC)
Shotton, J., Johnson, M., Cipolla, R., Semantic texton forests for image categorization and segmentation (2008) Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Shotton, J., Winn, J., Rother, C., Criminisi, A., TextonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation (2006) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 1-15. , DOI 10.1007/11744023-1, Computer Vision - ECCV 2006, 9th European Conference on Computer Vision, Proceedings
Shotton, J., Winn, J., Rother, C., Criminisi, A., Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling appearance, shape and context (2007) International Journal on Computer Vision
Verbeek, J., Triggs, B., Scene segmentation with conditional random fields learned from partially labeled images (2007) Proc. NIPS
Yin, P., Criminisi, A., Winn, J., Essa, I., Tree-based classifiers for bilayer video segmentation (2007) Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p. 4270033. , DOI 10.1109/CVPR.2007.383008, 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07