Feature Selection for Object Recognition by Property Related Analysis

Yong Mei Zhang; Li Ma

doi:10.4028/www.scientific.net/KEM.480-481.323

Paper Titles

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Analytical Expression of T-v_C Dependence in Standard ISO 3685 for Cutting Ceramic
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Feature Selection for Object Recognition by Property Related Analysis
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Experimental Research on Dynamic Characteristics of Synthetic Jet Induced by SJA with HCPCS Membrane
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HomeKey Engineering MaterialsKey Engineering Materials Vols. 480-481Feature Selection for Object Recognition by...

Feature Selection for Object Recognition by Property Related Analysis

Abstract:

The realization of object recognition is discussed, the intrinsic relation between factors through high step association analysis is presented to mine the various features using property related analysis, the sensitive recognition features are chosen to improve the recognition efficiency. A multi-agent object recognition model(MAORM) is established, which combines concurrency research results and the specific characteristics of multi-sensor remote sensing image recognition. A majority-decision algorithm based on multi-agent is proposed. Experiment results show the system can effectively identify the bridges, wharfs, ships and so on. Compared with a single remote sensing image, the system can distinguish targets with higher recognition.

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Periodical:

Key Engineering Materials (Volumes 480-481)

Pages:

323-328

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.480-481.323

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Online since:

June 2011

Authors:

Yong Mei Zhang, Li Ma

Keywords:

Association Analysis, Data Mining (DM), Object Recognition

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References

[1] P Kakumanu, S Makrogiannis, N Bourbakis. A survey of skin-color modeling and detection rnethods. Pattern Recognition. Vol. 40 (2007), p.1106.

DOI: 10.1016/j.patcog.2006.06.010

Google Scholar

[2] Bakker, H.H.C., Flemmer, R.C. Data mining for generalised object recognition. Vol. 28(2009), p.369.

Google Scholar

[3] Fang Yu-Chun, Wang Yun-Hong, Tan Tie-Niu. Improving Face Detection through Fusion of Contour and Region Information. Chinese Journal of Computers. Vol. 27(2004), p.482.

Google Scholar

[4] Gonzalo Pajares, Jesús Manuel de la Cruz. A wavelet-based image fusion tutorial. Pattern Recognition. Vol. 37(2007), p.1855.

DOI: 10.1016/j.patcog.2004.03.010

Google Scholar

[5] Liu, Lin, Wang Yuehuan. Multi-agent Cooperation Method Based on Intention Recognition. Image and Signal Processing, 2008. CISP '08. Congress. Vol. 8(2009), p.216.

DOI: 10.1109/cisp.2008.564

Google Scholar