A probabilistic associative model for segmenting weakly supervised images

Zhang, L; Yang, Y; Gao, Y; Yu, Y; Wang, C; Li, X

A probabilistic associative model for segmenting weakly supervised images

Zhang, L Yang, Y

Gao, Y Yu, Y Wang, C Li, X

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Image Processing, 2014, 23 (9), pp. 4150 - 4159
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Zhang, L	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	Gao, Y	en_US
dc.contributor.author	Yu, Y	en_US
dc.contributor.author	Wang, C	en_US
dc.contributor.author	Li, X	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	IEEE Transactions on Image Processing, 2014, 23 (9), pp. 4150 - 4159	en_US
dc.identifier.issn	1057-7149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/115887
dc.description.abstract	Weakly supervised image segmentation is an important yet challenging task in image processing and pattern recognition fields. It is defined as: in the training stage, semantic labels are only at the image-level, without regard to their specific object/scene location within the image. Given a test image, the goal is to predict the semantics of every pixel/superpixel. In this paper, we propose a new weakly supervised image segmentation model, focusing on learning the semantic associations between superpixel sets (graphlets in this paper). In particular, we first extract graphlets from each image, where a graphlet is a small-sized graph measures the potential of multiple spatially neighboring superpixels (i.e., the probability of these superpixels sharing a common semantic label, such as the sky or the sea). To compare different-sized graphlets and to incorporate image-level labels, a manifold embedding algorithm is designed to transform all graphlets into equal-length feature vectors. Finally, we present a hierarchical Bayesian network to capture the semantic associations between postembedding graphlets, based on which the semantics of each superpixel is inferred accordingly. Experimental results demonstrate that: 1) our approach performs competitively compared with the state-of-the-art approaches on three public data sets and 2) considerable performance enhancement is achieved when using our approach on segmentation-based photo cropping and image categorization. © 2014 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Image Processing	en_US
dc.relation.isbasedon	10.1109/TIP.2014.2344433	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A probabilistic associative model for segmenting weakly supervised images	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.citation.volume	23	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	23	en_US

Abstract:

Weakly supervised image segmentation is an important yet challenging task in image processing and pattern recognition fields. It is defined as: in the training stage, semantic labels are only at the image-level, without regard to their specific object/scene location within the image. Given a test image, the goal is to predict the semantics of every pixel/superpixel. In this paper, we propose a new weakly supervised image segmentation model, focusing on learning the semantic associations between superpixel sets (graphlets in this paper). In particular, we first extract graphlets from each image, where a graphlet is a small-sized graph measures the potential of multiple spatially neighboring superpixels (i.e., the probability of these superpixels sharing a common semantic label, such as the sky or the sea). To compare different-sized graphlets and to incorporate image-level labels, a manifold embedding algorithm is designed to transform all graphlets into equal-length feature vectors. Finally, we present a hierarchical Bayesian network to capture the semantic associations between postembedding graphlets, based on which the semantics of each superpixel is inferred accordingly. Experimental results demonstrate that: 1) our approach performs competitively compared with the state-of-the-art approaches on three public data sets and 2) considerable performance enhancement is achieved when using our approach on segmentation-based photo cropping and image categorization. © 2014 IEEE.

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http://hdl.handle.net/10453/115887