Multiview discriminative geometry preserving projection for image classification.
In many image classification applications, it is common to extract multiple visual features from different views to describe an image. Since different visual features have their own specific statistical properties and discriminative powers for image classification, the conventional solution for mult...
| Publicado en: | Scientific World Journal pp. 924090 - 924091 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103818685&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103818685 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2014 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 103818685 103818685 NLM24737997 2012553194 10.1155/2014/924090 NLM24737997 PMC3967461 103818685 ppf: 924090 ppct: 1 formats: tig: atl: Multiview discriminative geometry preserving projection for image classification. aug: au: Wang, Ziqiang Sun, Xia Sun, Lijun Huang, Yuchun affil: School of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China. sug: subj: Algorithms Image Interpretation, Computer Assisted Methods Information Science ab: In many image classification applications, it is common to extract multiple visual features from different views to describe an image. Since different visual features have their own specific statistical properties and discriminative powers for image classification, the conventional solution for multiple view data is to concatenate these feature vectors as a new feature vector. However, this simple concatenation strategy not only ignores the complementary nature of different views, but also ends up with "curse of dimensionality." To address this problem, we propose a novel multiview subspace learning algorithm in this paper, named multiview discriminative geometry preserving projection (MDGPP) for feature extraction and classification. MDGPP can not only preserve the intraclass geometry and interclass discrimination information under a single view, but also explore the complementary property of different views to obtain a low-dimensional optimal consensus embedding by using an alternating-optimization-based iterative algorithm. Experimental results on face recognition and facial expression recognition demonstrate the effectiveness of the proposed algorithm. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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