Discriminative cellets discovery for fine-grained image categories retrieval

Zhang, L; Yang, Y; Zimmermann, R

Discriminative cellets discovery for fine-grained image categories retrieval

Zhang, L Yang, Y

Zimmermann, R

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Publication Type:: Conference Proceeding
Citation:: ICMR 2014 - Proceedings of the ACM International Conference on Multimedia Retrieval 2014, 2014, pp. 57 - 64
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	Zimmermann, R	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	ICMR 2014 - Proceedings of the ACM International Conference on Multimedia Retrieval 2014, 2014, pp. 57 - 64	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120911
dc.description.abstract	Fine-grained image categories recognition is a challenging task aiming at distinguishing objects belonging to the same basic-level category, such as leaf or mushroom. It is a useful technique that can be applied for species recognition, face verification, and etc. Most of the existing methods have difficulties to automatically detect discriminative object components. In this paper, we propose a new fine- grained image categorization model that can be deemed as an improved version spatial pyramid matching (SPM). In- stead of the conventional SPM that enumeratively conducts cell-to-cell matching between images, the proposed model combines multiple cells into cellets that are highly responsive to object fine-grained categories. In particular, we describe object components by cellets that connect spatially adjacent cells from the same pyramid level. Straightforwardly, image categorization can be casted as the matching between cellets extracted from pairwise images. Toward an effective matching process, a hierarchical sparse coding algorithm is derived that represents each cellet by a linear combination of the basis cellets. Further, a linear discriminant analy- sis (LDA)-like scheme is employed to select the cellets with high discrimination. On the basis of the feature vector built from the selected cellets, fine-grained image categorization is conducted by training a linear SVM. Experimental results on the Caltech-UCSD birds, the Leeds butterflies, and the COSMIC insects data sets demonstrate our model out- performs the state-of-the-art. Besides, the visualized cellets show discriminative object parts are localized accurately. Copyright 2014 ACM.	en_US
dc.relation.ispartof	ICMR 2014 - Proceedings of the ACM International Conference on Multimedia Retrieval 2014	en_US
dc.relation.isbasedon	10.1145/2578726.2578736	en_US
dc.title	Discriminative cellets discovery for fine-grained image categories retrieval	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	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.publication-status	Published	en_US

Abstract:

Fine-grained image categories recognition is a challenging task aiming at distinguishing objects belonging to the same basic-level category, such as leaf or mushroom. It is a useful technique that can be applied for species recognition, face verification, and etc. Most of the existing methods have difficulties to automatically detect discriminative object components. In this paper, we propose a new fine- grained image categorization model that can be deemed as an improved version spatial pyramid matching (SPM). In- stead of the conventional SPM that enumeratively conducts cell-to-cell matching between images, the proposed model combines multiple cells into cellets that are highly responsive to object fine-grained categories. In particular, we describe object components by cellets that connect spatially adjacent cells from the same pyramid level. Straightforwardly, image categorization can be casted as the matching between cellets extracted from pairwise images. Toward an effective matching process, a hierarchical sparse coding algorithm is derived that represents each cellet by a linear combination of the basis cellets. Further, a linear discriminant analy- sis (LDA)-like scheme is employed to select the cellets with high discrimination. On the basis of the feature vector built from the selected cellets, fine-grained image categorization is conducted by training a linear SVM. Experimental results on the Caltech-UCSD birds, the Leeds butterflies, and the COSMIC insects data sets demonstrate our model out- performs the state-of-the-art. Besides, the visualized cellets show discriminative object parts are localized accurately. Copyright 2014 ACM.

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http://hdl.handle.net/10453/120911