Abstract
Millions of place-specified photos are uploaded on the Internet. Landmark labeling is very important for place-specified image understanding, landmark retrieval and auto-annotation. In this paper, we aim at extracting and labeling a Landmark in an image. The novelty of our method is that we use multi-layer superpixels to effectively extract a Landmark. The multi-layer superpixels can be used to capture the context of scale space and the spatial coherency of neighboring superpixels. And the context constraints are enforced by Conditional Random Field. In our method, we firstly learn a SVM classifier which operates on the superpixels of the training data. Then we construct a 3D adjacent graph which links the superpixels not only in the same layer but also in the successive layers. Finally, we use Conditional Random Field to combine the supervision information with the context cues in order to label landmarks. We compare our method with the state-of-the-art methods on the landmark images which are collected from Flickr, and the experimental results show that our method has achieved the best detection precision and the best pixel-based precision-recall.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, pp. I-511–I-518 (2001)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceeding of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893 (2005)
Fergus, R., Perona, P., Zisserman, A.: A Visual Category Filter for Google Images. In: Pajdla, T., Matas, J. (eds.) ECCV 2004, Part I. LNCS, vol. 3021, pp. 242–256. Springer, Heidelberg (2004)
Fergus, R., Fei-Fei, L., Perona, P., Zisserman, A.: Learning object categories from google’s image search. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, October 17-21, vol. 2, pp. 1816–1823 (2005)
Li, J., Wang, G., Fei-Fei, L.: OPTIMOL: automatic object picture collection via incremental model learning. Computer Vision and Pattern Recognition (2006)
Shotton, J., Johnson, M., Cipolla, R.: Semantic texton forests for image categorization and segmentation. In: CVPR, pp. 1–8 (2008)
Fulkerson, B., Vedaldi, A., Soatto, S.: Class segmentation and object localization with superpixel neighborhoods. In: ICCV, pp. 670–677 (2009)
Snavely, N., Seitz, S., Szeliski, R.: Photo tourism: Exploring photo collections in 3D. ACM Trans. on Graphics 25(3) (2006)
Snavely, N., Seitz, S.M., Szeliski, R.: Modeling the world from Internet photo collections. International Journal of Computer Vision 80(2), 189–210 (2008)
Simon, I., Snavely, N., Seitz, S.M.: Scene summarization for online image collections. In: ICCV (2007)
Li, X., Wu, C., Zach, C., Lazebnik, S., Frahm, J.-M.: Modeling and Recognition of Landmark Image Collections Using Iconic Scene Graphs. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 427–440. Springer, Heidelberg (2008)
Li, Y., Crandall, D.J., Huttenlocher, D.P.: Landmark classification in large-scale image collections. In: ICCV (2009)
Quack, T., Leibe, B., Van Gool, L.: World-scale Mining of Objects and Events from Community Photo Collections. In: CIVR 2008, Niagara Falls, Canada, July 7-9 (2008)
Gammeter, S., Bossard, L., Quack, T., Van Gool, L.: I know what you did last summer: object-level auto-annotation of holiday snaps. In: ICCV 2009, Kyoto, Japan, September 27-October 4 (2009)
Liu, H., Qu, Y.: Exploiting context aware category discovery for image labeling. In: Proceedings of the Third International Conference on Internet Multimedia Computing and Service (2011)
Galleguillos, C., Babenko, B., Rabinovich, A., Belongie, S.: Weakly Supervised Object Localization with Stable Segmentations. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 193–207. Springer, Heidelberg (2008)
Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional Random Fields: Probabilistic models for segmenting and labeling sequence data. In: ICML, pp. 282–289 (2001)
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vision 42(3), 145–175 (2001)
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC Superpixels (2010), http://ivrg.epfl.ch/supplementary_material/RK_SLICSuperpixels/index.html
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60, 91–110 (2004)
Murphy, K.P., Weiss, Y., Jordan, M.I.: Loopy belief propagation for approximate inference: an empirical study. In: Uncertainty in Artificial Intelligence, Sweden, pp. 467–475 (1999)
Ji, R., Duan, L., Chen, J., Yao, H., Yuan, J., Rui, Y., Gao, W.: Location discriminative vocabulary coding for mobile landmark search. Int. J. Comput. Vision 96(3), 290–314 (2012)
Gao, Y., Wang, M., Zha, Z., Shen, J., Li, X., Wu, X.: Visual-textual joint relevance learning for tag-based social image search. IEEE Transactions on Image Processing (in press)
Gao, Y., Wang, M., Zha, Z., Shen, J., Tian, Q., Dai, Q., Zhang, N.: Less is more: Efficient 3D object retrieval with query view selection. IEEE Transactions on Multimedia 13(5), 1007–1018 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Qu, Y., Yang, J., Liu, H., Xie, Y., Li, C. (2012). Instance-Level Landmark Labeling via Multi-layer Superpixels. In: Lin, W., et al. Advances in Multimedia Information Processing – PCM 2012. PCM 2012. Lecture Notes in Computer Science, vol 7674. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34778-8_67
Download citation
DOI: https://doi.org/10.1007/978-3-642-34778-8_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34777-1
Online ISBN: 978-3-642-34778-8
eBook Packages: Computer ScienceComputer Science (R0)