Abstract
The development of classifiers for object detection in images is a complex task that comprises the creation of representative and potentially large datasets from a target object by repetitive and time-consuming intellectual annotations, followed by a sequence of methods to train, evaluate and optimize the generated classifier. This is conventionally achieved by the usage and combination of many different tools. Here, we present a holistic approach to this scenario by providing a unified tool that covers the single development stages in one solution to facilitate the development process. We prove this concept by the example of creating a face detection classifier.
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Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes (VOC) Challenge. International Journal of Computer Vision 88(2), 303–338 (2010)
Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: ImageNet: A large-scale hierarchical image database. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 248–255 (2009)
Miller, G.A.: WordNet: A Lexical Database for English. Communications of the ACM 38(11), 39–41 (1995)
Jain, A.K., Duin, R.P.W., Gregory, R.L. (eds.): The Oxford Companion to the Mind, 2nd edn., pp. 698–703. Oxford University Press, Oxford (2004)
Schneiderman, H.A.: Statistical method for 3D object detection applied to faces and cars. PhD Thesis, Carnegie Mellon University (2000)
Huang, C., Ai, H., Li, Y., Lao, S.: High-Performance Rotation Invariant Multiview Face Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(4), 671–686 (2007)
Phillips, P., Moon, H., Rizvi, S., Rauss, P.: The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1090–1104 (2000)
Angelova, A., Abu-Mostafam, Y., Perona, P.: Pruning training sets for learning of object categories. In: IEEE International Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA, pp. 494–501 (2005)
Georghiades, A., Belhumeur, P., Kriegman, D.: From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), 643–660 (2001)
Sim, T., Baker, S., Bsat, M.: The CMU Pose, Illumination, and Expression (PIE) database. In: Fifth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 46–51 (2002)
Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-PIE. Journal Image and Vision Computing 28(5), 807–813 (2010)
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. Journal Computer Vision and Image Understanding 106(1), 59–70 (2007)
Griffin, G., Holub, A., Perona, P.: Caltech-256 Object Category Dataset. California Institute of Technology. Technical Report 7694 (2007)
Russell, B., Torralba, A., Murphy, K., Freeman, W.: LabelMe: a database and web-based tool for image annotation. International Journal of Computer Vision 77(1), 157–173 (2008)
Ahn, L., von, D.L.: Labeling images with a computer game. In: Proceedings of the 2004 Conference on Human Factors in Computing Systems, pp. 319–326 (2004)
Yao, B., Yang, X., Zhu, S.-C.: Introduction to a large-scale general purpose ground truth database: Methodology, annotation tool and benchmarks. In: Yuille, A.L., Zhu, S.-C., Cremers, D., Wang, Y. (eds.) EMMCVPR 2007. LNCS, vol. 4679, pp. 169–183. Springer, Heidelberg (2007)
Ladicky, L., Russell, C., Kohli, P., Torr, P.H.S.: Graph cut based inference with co-occurrence statistics. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part V. LNCS, vol. 6315, pp. 239–253. Springer, Heidelberg (2010)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(11), 1222–1239 (2001)
Doermann, D., Mihalcik, D.: Tools and Techniques for Video Performance Evaluation. In: Proc. 15th International Conference on Pattern Recognition, vol. 4, pp. 167–170 (2000)
Lachiche, N., Flach, P.A.: Improving Accuracy and Cost of Two-class and Multi-class Probabilistic Classifiers Using ROC Curves. In: 20th International Conference on Machine Learning, pp. 416–423 (2003)
Tanner, W.P.J.R., Swets, J.A., Welch, H.W.: A New Theory of Visual Detection. Defense Technical Information Center, Electronic Defense Group, University of Michigan. Technical Reports, p. 42 (1953)
Metz, C.E.: Receiver operating characteristic analysis: A tool for the quantitative evaluation of observer performance and imaging systems. Journal of the American College Radiology 3(6), 413–422 (2006)
World Meteorological Organization (Eds.): Manual on the Global Data Processing System, part II, Attachments II.7 and II.8. 2010, Updated in 2012. Switzerland, p. 193 (2012)
Provost, F.J., Fawcett, T.: Robust Classification for Imprecise Environments. Machine Learning 42(3), 203–231 (2001)
Schreiner, C., Zhang, H., Guerrero, C., Torkkola, K., Zhang, K.: A Semi-Automatic Data Annotation Tool for Driving Simulator Data Reduction. In: Driving Simulation Conference, North America, p. 9 (2007)
Meudt, S., Bigalke, L., Schwenker, F.: Atlas Annotation tool using partially supervised learning and multi-view co-learning in human-computer-interaction scenarios. In: 11th International Conference on Information Science, Signal Processing and their Applications, pp. 1309–1312 (2012)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1), 10–18 (2009)
Shafait, F., Reif, M., Kofler, C., Breuel, T.: Pattern Recognition Engineering. In: RapidMiner Community Meeting and Conference, Dortmund, Germany (2010)
Chang, H.J., Yi, K.M., Yin, S., Kim, S.W., Baek, Y.M., Ahn, H.S., Choi, J.Y.: PIL-EYE: Integrated System for Sustainable Development of Intelligent Visual Surveillance Algorithms. In: IEEE Digital Image Computing: Techniques and Applications, pp. 231–236 (2011)
Sung, K.K., Poggio, T.: Example-based learning for view-based human face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(1), 39–51 (1998)
Schapire, R., Freund, Y.: A decision theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)
Schneiderman, H.: Learning statistical structure for object detection. In: Petkov, N., Westenberg, M.A. (eds.) CAIP 2003. LNCS, vol. 2756, pp. 434–441. Springer, Heidelberg (2003)
Rowley, H., Baluja, S., Kanade, T.: Neural network-based face detection. In: International Conference on Computer Vision and Pattern Recognition, pp. 203–208 (1996)
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Storz, M., Ritter, M., Manthey, R., Lietz, H., Eibl, M. (2013). Annotate. Train. Evaluate. A Unified Tool for the Analysis and Visualization of Workflows in Machine Learning Applied to Object Detection. In: Kurosu, M. (eds) Human-Computer Interaction. Towards Intelligent and Implicit Interaction. HCI 2013. Lecture Notes in Computer Science, vol 8008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39342-6_22
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