Please use this identifier to cite or link to this item:
|Title:||Image decomposition with multilabel context: Algorithms and applications|
|Citation:||Li, T., Yan, S., Mei, T., Hua, X.-S., Kweon, I.-S. (2011-08). Image decomposition with multilabel context: Algorithms and applications. IEEE Transactions on Image Processing 20 (8) : 2301-2314. ScholarBank@NUS Repository. https://doi.org/10.1109/TIP.2010.2103081|
|Abstract:||Most research on image decomposition, e.g., image segmentation and image parsing, has predominantly focused on the low-level visual clues within a single image and neglected the contextual information across images. In this paper, we present a new perspective to image decomposition piloted by the multilabel context associated with each individual image. Observing that the contextual information (i.e., local label representations of the same label are similar while those from different labels are dissimilar) exists across images, we propose to perform image decomposition in a collective way and obtain an optimal representation for each label from a set of multilabeled images. We formulate the problem as an optimization problem which maximizes inter-label difference while minimizing the intra-label difference of the target label representations and propose two ways to solve this problem. Such a contextual image decomposition has a wide variety of applications, among which two exemplary onesmultilabel image annotation and label ranking, are presented and evaluated with different classification techniques. Extensive experiments on two benchmark datasets demonstrate promising results. © 2010 IEEE.|
|Source Title:||IEEE Transactions on Image Processing|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Oct 15, 2018
WEB OF SCIENCETM
checked on Oct 8, 2018
checked on Sep 22, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.