iccv iccv2013 iccv2013-6 iccv2013-6-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Ehsan Elhamifar, Guillermo Sapiro, Allen Yang, S. Shankar Sasrty
Abstract: In many image/video/web classification problems, we have access to a large number of unlabeled samples. However, it is typically expensive and time consuming to obtain labels for the samples. Active learning is the problem of progressively selecting and annotating the most informative unlabeled samples, in order to obtain a high classification performance. Most existing active learning algorithms select only one sample at a time prior to retraining the classifier. Hence, they are computationally expensive and cannot take advantage of parallel labeling systems such as Mechanical Turk. On the other hand, algorithms that allow the selection of multiple samples prior to retraining the classifier, may select samples that have significant information overlap or they involve solving a non-convex optimization. More importantly, the majority of active learning algorithms are developed for a certain classifier type such as SVM. In this paper, we develop an efficient active learning framework based on convex programming, which can select multiple samples at a time for annotation. Unlike the state of the art, our algorithm can be used in conjunction with any type of classifiers, including those of the fam- ily of the recently proposed Sparse Representation-based Classification (SRC). We use the two principles of classifier uncertainty and sample diversity in order to guide the optimization program towards selecting the most informative unlabeled samples, which have the least information overlap. Our method can incorporate the data distribution in the selection process by using the appropriate dissimilarity between pairs of samples. We show the effectiveness of our framework in person detection, scene categorization and face recognition on real-world datasets.
[1] S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, 2010.
[2] K. Brinker. Incorporating diversity in active learning with support vector machines. ICML, 2003.
[3] E. Chang, S. Tong, K. Goh, , and C. Chang. Support vector machine concept-dependent active learning for image retrieval. IEEE Trans. on Multimedia, 2005.
[4] D. Cohn, L. Atlas, and R. Ladner. Improving generalization with active learning. Journal of Machine Learning, 1994.
[5] B. Collins, J. Deng, K. Li, and L. Fei-Fei. Towards scalable dataset construction: An active learning approach. ECCV, 2008.
[6] N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. CVPR, 2005.
[7] J. Deng, W. Dong, R. Socher, L. J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. CVPR, 2009.
[8] P. Doll a´r, C. Wojek, B. Schiele, and P. Perona. Pedestrian detection: A benchmark. CVPR, 2009.
[9] E. Elhamifar, G. Sapiro, and R. Vidal. Finding exemplars from pairwise dissimilarities via simultaneous sparse recovery. NIPS, 2012.
[10] E. Elhamifar and S. S. Sastry. Dissimilarity-based sparse modeling representative selection. submitted to IEEE Trans. PAMI, 2013.
[11] R. Greiner, A. Grove, and D. Roth. Learning cost-sensitive active classiers. Articial Intelligence, 2002.
[12] Y. Guo and D. Schuurmans. Discriminative batch mode active learning. NIPS, 2007.
[13] S. Hoi, R. Jin, J. Zhu, and M. Lyu. Semi-supervised svm batch mode active learning with applications to image retrieval. ACM Trans. on Information Systems, 2009.
[14] T. K. Huang, R. C. Weng, and C. J. Lin. Generalized bradley-terry models and multi-class probability estimates. JMLR, 2006.
[15] A. Joshi, F. Porikli, and N. Papanikolopoulos. Multi-class active learning for image classification. IEEE CVPR, 2009.
[16] A. Kapoor, K. Grauman, R. Urtasun, and T. Darrell. Active learning with gaussian processes for object categorization. ICCV, 2007.
[17] A. Kovashka, S. Vijayanarasimhan, and K. Grauman. Actively selecting annotations among objects and attributes. ICCV, 2011.
[18] A. Krause and C. Guestrin. Nonmyopic active learning of gaussian processes: an exploration-exploitation approach. ICML, 2007.
[19] S. Lazebnik, C.Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. CVPR, 2006.
[20] K. C. Lee, J. Ho, and D. Kriegman. Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. PAMI, 2005.
[21] K. Nigam, A. McCallum, S. Thrun, and T. Mitchell. Text classification from labeled and unlabeled documents using em. Journal of Machine Learning, 2000.
[22] G. J. Qi, X. S. Hua, Y. Rui, J. Tang, and H. J. Zhang. Twodimensional active learning for image classification. CVPR, 2008.
[23] N. Roy and A. McCallum. Toward optimal active learning through sampling estimation of error reduction. ICML, 2001.
[24] B. Russell, A. Torralba, K. Murphy, and W. Freeman. Labelme: a database and web-based tool for image annotation. IJCV, 2008.
[25] G. Schohn and D. Cohn. Less is more: Active learning with support vector machines. ICML, 2000.
[26] B. Settles and M. Craven. An analysis of active learning strategies for sequence labeling tasks. Conference on Empirical Methods in Natural Language Processing, 2008.
[27] B. Siddiquie and A. Gupta. Beyond active noun tagging: Modeling contextual interactions for multi-class active learning. CVPR, 2010.
[28] A. Sorokin and D. Forsyth. Utility data annotation with amazon mechanical turk. CVPR, 2008.
[29] S. Tong and D. Koller. Support vector machine active learning with applications to text classification. JMLR, 2001.
[30] S. Vijayanarasimhan and K. Grauman. Multi-level active prediction of useful image annotations for recognition. NIPS, 2008. [3 1] S. Vijayanarasimhan and K. Grauman. Whats it going to cost you?: Predicting effort vs. informativeness for multi-label image annotations. CVPR, 2009.
[32] S. Vijayanarasimhan and K. Grauman. Large-scale live active learning: Training object detectors with crawled data and crowds. CVPR, 2011.
[33] S. Vijayanarasimhan and K. Grauman. Active frame selection for label propagation in videos. ECCV, 2012.
[34] S. Vijayanarasimhan, P. Jain, and K. Grauman. Far-sighted active learning on a budget for image and video recognition. CVPR, 2010.
[35] J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma. Robust face recognition via sparse representation. IEEE Trans. PAMI, 2009.
[36] Z. Xu, R. Akella, and Y. Zhang. Incorporating diversity and density in active learning for relevance feedback. European Conference on Information Retrieval, 2007.
[37] R. Yan, J. Yang, and A. Hauptmann. Automatically labeling video data using multiclass active learning. ICCV, 2003.
[38] C. Zhang and T. Chen. An active learning framework for contentbased information retrieval. IEEE Trans. on Multimedia, 2002. 216