Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICCV.2011.6126528
Title: Multi-task low-rank affinity pursuit for image segmentation
Authors: Cheng, B.
Liu, G. 
Wang, J.
Huang, Z.
Yan, S. 
Issue Date: 2011
Source: Cheng, B.,Liu, G.,Wang, J.,Huang, Z.,Yan, S. (2011). Multi-task low-rank affinity pursuit for image segmentation. Proceedings of the IEEE International Conference on Computer Vision : 2439-2446. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCV.2011.6126528
Abstract: This paper investigates how to boost region-based image segmentation by pursuing a new solution to fuse multiple types of image features. A collaborative image segmentation framework, called multi-task low-rank affinity pursuit, is presented for such a purpose. Given an image described with multiple types of features, we aim at inferring a unified affinity matrix that implicitly encodes the segmentation of the image. This is achieved by seeking the sparsity-consistent low-rank affinities from the joint decompositions of multiple feature matrices into pairs of sparse and low-rank matrices, the latter of which is expressed as the production of the image feature matrix and its corresponding image affinity matrix. The inference process is formulated as a constrained nuclear norm and ℓ 2,1-norm minimization problem, which is convex and can be solved efficiently with the Augmented Lagrange Multiplier method. Compared to previous methods, which are usually based on a single type of features, the proposed method seamlessly integrates multiple types of features to jointly produce the affinity matrix within a single inference step, and produces more accurate and reliable segmentation results. Experiments on the MSRC dataset and Berkeley segmentation dataset well validate the superiority of using multiple features over single feature and also the superiority of our method over conventional methods for feature fusion. Moreover, our method is shown to be very competitive while comparing to other state-of-the-art methods. © 2011 IEEE.
Source Title: Proceedings of the IEEE International Conference on Computer Vision
URI: http://scholarbank.nus.edu.sg/handle/10635/83993
ISBN: 9781457711015
DOI: 10.1109/ICCV.2011.6126528
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