Abstract
Multiple Instance Learning (MIL) provides a framework for training a discriminative classifier from data with ambiguous labels. This framework is well suited for the task of learning object classifiers from weakly labeled image data, where only the presence of an object in an image is known, but not its location. Some recent work has explored the application of MIL algorithms to the tasks of image categorization and natural scene classification. In this paper we extend these ideas in a framework that uses MIL to recognize and localize objects in images. To achieve this we employ state of the art image descriptors and multiple stable segmentations. These components, combined with a powerful MIL algorithm, form our object recognition system called MILSS. We show highly competitive object categorization results on the Caltech dataset. To evaluate the performance of our algorithm further, we introduce the challenging Landmarks-18 dataset, a collection of photographs of famous landmarks from around the world. The results on this new dataset show the great potential of our proposed algorithm.
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Keywords
- Image Categorization
- Object Categorization
- Salient Region
- Average Categorization Accuracy
- Multiple Instance Learn
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Fergus, R., Perona, P., Zisserman, A.: Weakly supervised scale-invariant learning of models for visual recognition. IJCV 71(3), 273–303 (2007)
Opelt, A., Fussenegger, M., Auer, P.: Generic object recognition with boosting. PAMI 28(3), 416–431 (2006)
Russell, B., Efros, A., Sivic, J., Freeman, W., Zisserman, A.: Using multiple segmentations to discover objects and their extent in image collections. In: CVPR (2006)
Sivic, J., Russell, B.C., Efros, A.A., Zisserman, A., Freeman, W.T.: Discovering object categories in image collections. In: CVPR (2005)
Todorovic, S., Ahuja, N.: Extracting subimages of an unknown category from a set of images. In: CVPR (2006)
Bar-Hillel, A., Hertz, T., Weinshall, D.: Object class recognition by boosting a part-based model. In: CVPR (2005)
Crandall, D., Huttenlocher, D.: Weakly supervised learning of part-based spatial models for visual object recognition. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 16–29. Springer, Heidelberg (2006)
Wang, G., Zhang, Y., Fei-Fei, L.: Using dependent regions for object categorization in a generative framework. In: CVPR (2006)
Chen, Y., Bi, J., Wang, J.: MILES: Multiple-instance learning via embedded instance selection. PAMI 28(12), 1931–1947 (2006)
Qi, G., Hua, X., Rui, Y., Mei, T., Tang, J., Zhang, H.: Concurrent multiple instance learning for image categorization. In: CVPR (2007)
Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: CVPR (2003)
Dietterich, T.G., Lathrop, R.H., Perez, L.T.: Solving the multiple-instance problem with axis parallel rectangles. AAAI, Menlo Park (1997)
Andrews, S., Hofmann, T., Tsochantaridis, I.: Multiple instance learning with generalized support vector machines. AAAI, Menlo Park (2002)
Viola, P., Platt, J.C., Zhang, C.: Multiple instance boosting for object detection. In: NIPS, vol. 18 (2006)
Maron, O., Ratan, A.: Multiple-instance learning for natural scene classification. In: ICML (1998)
Zhou, Z., Zhang, M.: Multi-instance multi-label learning with application to scene classification. In: NIPS, vol. 19 (2007)
Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: NIPS, vol. 15 (2002)
Yang, C., Dong, M., Hua, J.: Region-based image annotation using asymmetrical support vector machine-based multi-instance learning. In: CVPR (2006)
Chen, Y., Wang, J.: Image categorization by learning and reasoning with regions. JMLR 5, 913–939 (2004)
Bi, J., Chen, Y., Wang, J.: A sparse support vector machine approach to region-based image categorization. In: CVPR (2005)
Friedman, J.H.: Greedy function approximation: A gradient boosting machine. The Annals of Statistics 29(5), 1189–1232 (2001)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. JCSS 55, 119–139 (1997)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR (2001)
Kadir, T., Brady, M.: Saliency, scale and image description. IJCV 45 (2001)
Carson, C., Belongie, S., Greenspan, H., Malik, J.: Blobworld: image segmentation using expectation-maximization and its application to image querying. PAMI 24(8), 1026–1038 (2002)
Deng, Y., Manjunath, B.: Unsupervised segmentation of color-texture regions in images and video. PAMI 23(8), 800–810 (2001)
Shi, J., Malik, J.: Normalized cuts and image segmentation. PAMI 22(8), 888–905 (2000)
Rabinovich, A., Lange, T., Buhmann, J., Belongie, S.: Model order selection and cue combination for image segmentation. In: CVPR (2006)
Rabinovich, A., Vedaldi, A., Belongie, S.: Does image segmentation improve object categorization? UCSD Technical Report CSE CS2007-0908 (2007)
Malisiewicz, T., Efros, A.: Improving spatial support for objects via multiple segmentations. BMVC (2007)
Roth, V., Ommer, B.: Exploiting low-level image segmentation for object recognition. In: Franke, K., Müller, K.-R., Nickolay, B., Schäfer, R. (eds.) DAGM 2006. LNCS, vol. 4174, pp. 11–20. Springer, Heidelberg (2006)
Rabinovich, A., Vedaldi, A., Galleguillos, C., Wiewora, E., Belongie, S.: Objects in context. In: ICCV (2007)
Malik, J., Belongie, S., Shi, J., Leung, T.: Textons, contours and regions: Cue integration in image segmentation. In: ICCV (1999)
Lowe, D.: Object recognition from local scale-invariant features. In: ICCV (1999)
Cour, T., Benezit, F., Shi, J.: Spectral segmentation with multiscale graph decomposition. In: CVPR (2005)
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Galleguillos, C., Babenko, B., Rabinovich, A., Belongie, S. (2008). Weakly Supervised Object Localization with Stable Segmentations. In: Forsyth, D., Torr, P., Zisserman, A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5302. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88682-2_16
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DOI: https://doi.org/10.1007/978-3-540-88682-2_16
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