Scene-oriented hierarchical classification of blurry and noisy images

A system for scene-oriented hierarchical classification of blurry and noisy images is proposed. It attempts to simulate important features of the human visual perception. The underlying approach is based on three strategies: extraction of essential signatures captured from a global context, simulati...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 21(2012), 5 vom: 21. Mai, Seite 2534-45
1. Verfasser: Dong, Le (VerfasserIn)
Weitere Verfasser: Su, Jiang, Izquierdo, Ebroul
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2012
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Journal Article Research Support, Non-U.S. Gov't
LEADER 01000naa a22002652 4500
001 NLM215415000
003 DE-627
005 20231224025506.0
007 cr uuu---uuuuu
008 231224s2012 xx |||||o 00| ||eng c
024 7 |a 10.1109/TIP.2012.2187528  |2 doi 
028 5 2 |a pubmed24n0718.xml 
035 |a (DE-627)NLM215415000 
035 |a (NLM)22334004 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Dong, Le  |e verfasserin  |4 aut 
245 1 0 |a Scene-oriented hierarchical classification of blurry and noisy images 
264 1 |c 2012 
336 |a Text  |b txt  |2 rdacontent 
337 |a ƒaComputermedien  |b c  |2 rdamedia 
338 |a ƒa Online-Ressource  |b cr  |2 rdacarrier 
500 |a Date Completed 15.08.2012 
500 |a Date Revised 30.04.2012 
500 |a published: Print-Electronic 
500 |a Citation Status MEDLINE 
520 |a A system for scene-oriented hierarchical classification of blurry and noisy images is proposed. It attempts to simulate important features of the human visual perception. The underlying approach is based on three strategies: extraction of essential signatures captured from a global context, simulating the global pathway; highlight detection based on local conspicuous features of the reconstructed image, simulating the local pathway; and hierarchical classification of extracted features using probabilistic techniques. The techniques involved in hierarchical classification use input from both the local and global pathways. Visual context is exploited by a combination of Gabor filtering with the principal component analysis. In parallel, a pseudo-restoration process is applied together with an affine invariant approach to improve the accuracy in the detection of local conspicuous features. Subsequently, the local conspicuous features and the global essential signature are combined and clustered by a Monte Carlo approach. Finally, clustered features are fed to a self-organizing tree algorithm to generate the final hierarchical classification results. Selected representative results of a comprehensive experimental evaluation validate the proposed system 
650 4 |a Journal Article 
650 4 |a Research Support, Non-U.S. Gov't 
700 1 |a Su, Jiang  |e verfasserin  |4 aut 
700 1 |a Izquierdo, Ebroul  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society  |d 1992  |g 21(2012), 5 vom: 21. Mai, Seite 2534-45  |w (DE-627)NLM09821456X  |x 1941-0042  |7 nnns 
773 1 8 |g volume:21  |g year:2012  |g number:5  |g day:21  |g month:05  |g pages:2534-45 
856 4 0 |u http://dx.doi.org/10.1109/TIP.2012.2187528  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |d 21  |j 2012  |e 5  |b 21  |c 05  |h 2534-45