Image collection exploration

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Image collection exploration is a mechanism to explore large digital image repositories. The huge amount of digital images produced every day through different devices such as mobile phones bring forth challenges for the storage, indexing and access to these repositories. Content-based image retrieval (CBIR) has been the traditional paradigm to index and retrieve images. However, this paradigm suffers of the well known semantic gap problem. Image collection exploration consists of a set of computational methods to represent, summarize, visualize and navigate image repositories in an efficient, effective and intuitive way.[1]

Summarization[edit]

Automatic summarization consists in finding a set of images from a larger image collection that represents such collection.[2] Different methods based on clustering have been proposed to select these image prototypes (summary). The summarization process addresses the problem of selecting a representative set of images of a search query or in some cases, the overview of an image collection.[3]

Visualization[edit]

Image collection visualization is the process of visualize a set of images using a visualization metaphor, in which an image similarity function is used to represent image relations in a visualization layout.[4] Information visualization is an active area that investigates new ways to visualize information by using visualization metaphors. Particularly, new ways of visualizing image collections are being investigated, which propose conventional [5] and unconventional [6] visualization metaphors.

Interaction[edit]

Image collection interaction consists in offering users mechanisms to feedback image search systems.[7] In this interaction process, the system learns from user feedback to retrieve results more precise and relevant to the user.

References[edit]

  1. ^ Jorge E. Camargo, Juan C. Caicedo, Fabio A. Gonzalez, A kernel-based framework for image collection exploration, Journal of Visual Languages & Computing, Volume 24, Issue 1, February 2013, 53-57. ISSN 10-45-926X. http://dx.doi.org/10.1016/j.jvlc.2012.10.008
  2. ^ Chunlei Yang, Jialie Shen, Jinye Peng, Jianping Fan, Image collection summarization via dictionary learning for sparse representation, Pattern Recognition, Volume 46, Issue 3, March 2013, Pages 948-961, ISSN 0031-3203, http://dx.doi.org/10.1016/j.patcog.2012.07.011.
  3. ^ Frompo.com (2013). Image search, Video search and Free Images. Frompo.com. ISBN 978-0-7611-7294-9. 
  4. ^ G.P. Nguyen, M. Worring, Interactive access to large image collections using similarity-based visualization, Journal of Visual Languages & Computing, Volume 19, Issue 2, April 2008, Pages 203-224, ISSN 1045-926X, http://dx.doi.org/10.1016/j.jvlc.2006.09.002.
  5. ^ Chaoli Wang, John P Reese, Huan Zhang, Jun Tao, Yi Gu, Jun Ma, and Robert J Nemiroff Similarity-based visualization of large image collections Information Visualization 1473871613498519, first published on August 6, 2013 doi:10.1177/1473871613498519
  6. ^ Marco Porta. 2006. Browsing large collections of images through unconventional visualization techniques. In Proceedings of the working conference on Advanced visual interfaces (AVI '06). ACM, New York, NY, USA, 440-444. DOI=10.1145/1133265.1133354 http://doi.acm.org/10.1145/1133265.1133354
  7. ^ Camargo, J.E.; Caicedo, J.C.; Chavarro, A.M.; Gonzaléz, F.A., "A kernel-based strategy for exploratory image collection search," Content-Based Multimedia Indexing (CBMI), 2010 International Workshop on , vol., no., pp.1,6, 23–25 June 2010. doi: 10.1109/CBMI.2010.5529893

External links[edit]