Abstract
Life-logging devices are becoming ubiquitous, yet still processing and extracting information from the vast amount of data that is being captured is a very challenging task. We propose a method to find discriminative regions which we define as regions that are salient, consistent, repetitive and discriminative. We explain our fast and novel algorithm to discover the discriminative regions and show different applications for discriminative regions such as summarization, classification and image search. Our experiments show that our algorithm is able to find discriminative regions and discriminative patches in a short time and extracts great results on our life-logging SenseCam dataset.
This is a preview of subscription content, log in via an institution to check access.
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
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, vol. 1. IEEE (2005)
Deselaers, T., Alexe, B., Ferrari, V.: Localizing objects while learning their appearance. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 452–466. Springer, Heidelberg (2010)
Doersch, C., Singh, S., Gupta, A., Sivic, J., Efros, A.A.: What makes paris look like paris? ACM Transactions on Graphics 31(4) (2012)
Dollár, P.: Piotr’s Image and Video Matlab Toolbox (PMT), http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html
Fathi, A., Farhadi, A., Rehg, J.M.: Understanding egocentric activities. In: ICCV. IEEE (2011)
Fathi, A., Ren, X., Rehg, J.M.: Learning to recognize objects in egocentric activities. In: CVPR. IEEE (2011)
Fritz, M., Schiele, B.: Decomposition, discovery and detection of visual categories using topic models. In: CVPR. IEEE (2008)
Girshick, R.B., Felzenszwalb, P.F., McAllester, D.: Discriminatively trained deformable part models, release 5, http://people.cs.uchicago.edu/~rbg/latent-release5/
Kang, H., Hebert, M., Kanade, T.: Discovering object instances from scenes of daily living. In: ICCV. IEEE (2011)
Kim, G., Faloutsos, C., Hebert, M.: Unsupervised modeling of object categories using link analysis techniques. In: CVPR. IEEE (2008)
Pirsiavash, H., Ramanan, D.: Detecting activities of daily living in first-person camera views. In: CVPR. IEEE (2012)
Russell, B.C., Freeman, W.T., Efros, A.A., Sivic, J., Zisserman, A.: Using multiple segmentations to discover objects and their extent in image collections. In: CVPR, vol. 2. IEEE (2006)
Singh, S., Gupta, A., Efros, A.A.: Unsupervised discovery of mid-level discriminative patches. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 73–86. Springer, Heidelberg (2012)
Sivic, J., Russell, B.C., Efros, A.A., Zisserman, A., Freeman, W.T.: Discovering object categories in image collections (2005)
Weber, M., Welling, M., Perona, P.: Towards automatic discovery of object categories. PAMI 2, 101–108 (2000)
Ye, J., Zhao, Z., Wu, M.: Discriminative k-means for clustering. NIPSÂ 20 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Moghimi, M., Kerr, J., Johnson, E., Godbole, S., Belongie, S. (2015). Discriminative Regions: A Substrate for Analyzing Life-Logging Image Sequences. In: He, X., Luo, S., Tao, D., Xu, C., Yang, J., Hasan, M.A. (eds) MultiMedia Modeling. MMM 2015. Lecture Notes in Computer Science, vol 8936. Springer, Cham. https://doi.org/10.1007/978-3-319-14442-9_42
Download citation
DOI: https://doi.org/10.1007/978-3-319-14442-9_42
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14441-2
Online ISBN: 978-3-319-14442-9
eBook Packages: Computer ScienceComputer Science (R0)