“AverageExplorer: interactive exploration and alignment of visual data collections” by Zhu, Lee and Efros

  • ©Jun-Yan Zhu, Yong Jae Lee, and Alexei A. Efros




    AverageExplorer: interactive exploration and alignment of visual data collections

Session/Category Title: Shady Images




    This paper proposes an interactive framework that allows a user to rapidly explore and visualize a large image collection using the medium of average images. Average images have been gaining popularity as means of artistic expression and data visualization, but the creation of compelling examples is a surprisingly laborious and manual process. Our interactive, real-time system provides a way to summarize large amounts of visual data by weighted average(s) of an image collection, with the weights reflecting user-indicated importance. The aim is to capture not just the mean of the distribution, but a set of modes discovered via interactive exploration. We pose this exploration in terms of a user interactively “editing” the average image using various types of strokes, brushes and warps, similar to a normal image editor, with each user interaction providing a new constraint to update the average. New weighted averages can be spawned and edited either individually or jointly. Together, these tools allow the user to simultaneously perform two fundamental operations on visual data: user-guided clustering and user-guided alignment, within the same framework. We show that our system is useful for various computer vision and graphics applications.


    1. Agarwala, A., Dontcheva, M., Agrawala, M., Drucker, S., Colburn, A., Curless, B., Salesin, D., and Cohen, M. 2004. Interactive digital photomontage. In SIGGRAPH. Google ScholarDigital Library
    2. Angelova, A., Abu-Mostafam, Y., and Perona, P. 2005. Pruning training sets for learning of object categories. In CVPR. Google ScholarDigital Library
    3. Balcan, M.-F., and Blum, A. 2008. Clustering with interactive feedback. In Algorithmic Learning Theory, Springer, 316–328. Google ScholarDigital Library
    4. Belhumeur, P. N., Jacobs, D. W., Kriegman, D. J., and Kumar, N. 2011. Localizing parts of faces using a consensus of exemplars. In CVPR. Google ScholarDigital Library
    5. Berg, T., and Berg, A. 2009. Finding iconic images. In 2nd Workshop on Internet Vision.Google Scholar
    6. Berg, T. L., Berg, A. C., and Shih, J. 2010. Automatic attribute discovery and characterization from noisy web data. In ECCV. Google ScholarDigital Library
    7. Boureau, Y.-L., Ponce, J., and LeCun, Y. 2010. A theoretical analysis of feature pooling in vision algorithms. In ICML.Google Scholar
    8. Campbell, J., 2002. http://jimcampbell.tv/.Google Scholar
    9. Carson, C., Belongie, S., Greenspan, H., and Malik, J. 2002. Blobworld: Image segmentation using expectation-maximization and its application to image querying. TPAMI. Google ScholarDigital Library
    10. Chen, T., Cheng, M.-M., Tan, P., Shamir, A., and Hu, S.-M. 2009. Sketch2photo: internet image montage. In SIGGRAPH Asia. Google ScholarDigital Library
    11. Dalal, N., and Triggs, B. 2005. Histograms of oriented gradients for human detection. In CVPR. Google ScholarDigital Library
    12. de Oliveira, M. C. F., and Levkowitz, H. 2003. From visual data exploration to visual data mining: A survey. TVCG.Google Scholar
    13. Divvala, S. K., Efros, A. A., and Hebert, M. 2012. How important are ‘deformable parts’ in the deformable parts model? In Parts and Attributes Workshop, ECCV. Google ScholarDigital Library
    14. Doersch, C., Singh, S., Gupta, A., Sivic, J., and Efros, A. A. 2012. What makes paris look like paris? In SIGGRAPH. Google ScholarDigital Library
    15. Gallagher, A. C., and Chen, T. 2008. Clothing cosegmentation for recognizing people. In CVPR.Google Scholar
    16. Galton, F. 1878. Composite portraits. Nature 18, 97–100.Google ScholarCross Ref
    17. Hariharan, B., Malik, J., and Ramanan, D. 2012. Discriminative decorrelation for clustering and classification. In ECCV. Google ScholarDigital Library
    18. Hays, J., and Efros, A. A. 2007. Scene completion using millions of photographs. In SIGGRAPH. Google ScholarDigital Library
    19. Hoai, M., and Zisserman, A. 2013. Discriminative sub-categorization. In CVPR. Google ScholarDigital Library
    20. Huang, G., Jain, V., and Learned-Miller, E. 2007. Unsupervised Joint Alignment of Complex Images. In ICCV.Google Scholar
    21. Huang, G. B., Ramesh, M., Berg, T., and Learned-Miller, E. 2007. Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. Tech. rep., University of Massachusetts, Amherst.Google Scholar
    22. Jacobs, C. E., Finkelstein, A., and Salesin, D. H. 1995. Fast multiresolution image querying. In SIGGRAPH. Google ScholarDigital Library
    23. Jesorsky, O., Kirchberg, K. J., and Frischholz, R. W. 2001. Robust face detection using the hausdorff distance. In Audio-and video-based biometric person authentication. Google ScholarDigital Library
    24. Kemelmacher-Shlizerman, I., Shechtman, E., Garg, R., and Seitz, S. M. 2011. Exploring photobios. In SIGGRAPH. Google ScholarDigital Library
    25. Khan, I., 2005. www.skny.com/artists/idris-khan/images/.Google Scholar
    26. Learned-Miller, E. 2006. Data Driven Image Models through Continuous Joint Alignment. TPAMI. Google ScholarDigital Library
    27. Lee, Y. J., Zitnick, C. L., and Cohen, M. F. 2011. Shadowdraw: real-time user guidance for freehand drawing. In SIGGRAPH. Google ScholarDigital Library
    28. Mattar, M., Hanson, A., and Learned-Miller, E. G. 2012. Unsupervised joint alignment and clustering using bayesian nonparametrics. In CVPR.Google Scholar
    29. Perona, P. 2010. Vision of a visipedia. 1526–1534.Google Scholar
    30. Pruszkowski, K., 1986. http://www.gallerywm.com/prusz_index.htmGoogle Scholar
    31. Salavon, J., 2004. http://cabinetmagazine.org/issues/15/salavon.php.Google Scholar
    32. Schaefer, S., McPhail, T., and Warren, J. 2006. Image deformation using moving least squares. In SIGGRAPH. Google ScholarDigital Library
    33. Shi, J., and Malik, J. 2000. Normalized Cuts and Image Segmentation. TPAMI. Google ScholarDigital Library
    34. Simon, I., Snavely, N., and Seitz, S. 2007. Scene Summarization for Online Image Collections. In ICCV.Google Scholar
    35. Singh, S., Gupta, A., and Efros, A. A. 2012. Unsupervised Discovery of Mid-Level Discriminative Patches. In ECCV. Google ScholarDigital Library
    36. Snavely, N., Seitz, S. M., and Szeliski, R. 2006. Photo tourism: Exploring photo collections in 3D. In SIGGRAPH. Google ScholarDigital Library
    37. Torralba, A., Bernal, H. J., Fergus, R., Weiss, Y., and Freeman, W., 2008. http://groups.csail.mit.edu/vision/tinyimages/.Google Scholar
    38. Torralba, A., 2001. http://people.csail.mit.edu/torralba/gallery/.Google Scholar
    39. Viégas, F. B., and Wattenberg, M. 2007. Artistic data visualization: Beyond visual analytics. In Online Communities and Social Computing. Springer, 182–191. Google ScholarDigital Library
    40. Zhang, W., Sun, J., and Tang, X. 2008. Cat head detection-how to effectively exploit shape and texture features. In ECCV.Google Scholar

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