Feature Adaptive Co-Segmentation by Complexity Awareness

In this paper, we propose a novel feature adaptive co-segmentation method that can learn adaptive features of different image groups for accurate common objects segmentation. We also propose image complexity awareness for adaptive feature learning. In the proposed method, the original images are fir...

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Vydáno v:	IEEE transactions on image processing Ročník 22; číslo 12; s. 4809 - 4824
Hlavní autoři:	Meng, Fanman, Li, Hongliang, Ngan, King Ngi, Zeng, Liaoyuan, Wu, Qingbo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.12.2013 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Adaptation models Algorithms Applied sciences Complexity Complexity theory Cosegmentation Detection, estimation, filtering, equalization, prediction distance metric learning Exact sciences and technology Feature extraction Fitness Image color analysis image complexity analysis Image detection Image processing Image segmentation Information, signal and communications theory Measurement Object detection Pattern recognition Segmentation Signal and communications theory Signal processing Signal representation. Spectral analysis Signal, noise State of the art Studies Telecommunications and information theory State of the art Image processing distance metric learning Error rate image complexity analysis Unsupervised classification Pattern recognition Shape detection Algorithm Signal classification Adaptive method Optimization Remote teaching Learning Image segmentation Image analysis Accuracy Least squares method Object detection Cosegmentation Metric Edge detection Target detection
ISSN:	1057-7149, 1941-0042, 1941-0042
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Popis
Shrnutí:	In this paper, we propose a novel feature adaptive co-segmentation method that can learn adaptive features of different image groups for accurate common objects segmentation. We also propose image complexity awareness for adaptive feature learning. In the proposed method, the original images are first ranked according to the image complexities that are measured by superpixel changing cue and object detection cue. Then, the unsupervised segments of the simple images are used to learn the adaptive features, which are achieved using an expectation-minimization algorithm combining l 1-regularized least squares optimization with the consideration of the confidence of the simple image segmentation accuracies and the fitness of the learned model. The error rate of the final co-segmentation is tested by the experiments on different image groups and verified to be lower than the existing state-of-the-art co-segmentation methods.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ObjectType-Article-2 ObjectType-Feature-1
ISSN:	1057-7149 1941-0042 1941-0042
DOI:	10.1109/TIP.2013.2278461