Paper Title:
Compressed Sensing Recognition Algorithm for Sonar Image Based on Non-Negative Matrix Factorization and Adjacency Spectra Feature
  Abstract

A compressed sensing (CS) recognition algorithm for sonar image based on non-negative matrix factorization and adjacency spectra (A-NMF) feature extraction is proposed in this paper. The feature vector of the tradition CS recognition algorithm is random Gaussian matrix, which causes the identification rate unstable. The stable feature vector which is extracted by the A-NMF feature extraction is used in this algorithm. The feature and structure of the original data can be expressed more accurately by this feature vector. Then the sonar images are classified under the compressed sensing framework. Experimental results show that the sonar image recognition is high, efficient and stable.

  Info
Periodical
Advanced Materials Research (Volumes 383-390)
Chapter
Chapter 22: Computer-Aided Engineering in Manufacturing
Edited by
Wu Fan
Pages
5818-5823
DOI
10.4028/www.scientific.net/AMR.383-390.5818
Citation
Y. L. Hao, L. Wang, "Compressed Sensing Recognition Algorithm for Sonar Image Based on Non-Negative Matrix Factorization and Adjacency Spectra Feature", Advanced Materials Research, Vols. 383-390, pp. 5818-5823, 2012
Online since
November 2011
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Wei Hong Xu, Min Zhu, Ya Ruo Jiang, Yu Shan Bai, Yan Yu
Chapter 10: Environmentally Sustainable Manufacturing Processes and Systems
Abstract:In this paper we present a spectral clustering method based on the MSRD (Most Similar Relation Diagram). The feature of this method is that...
2577
Authors: Tian Zhong Sui, Zhen Tan, Lei Wang, Xiao Bin Gu, Zhao Hui Ren
Chapter 8: Algoritms
Abstract:Dimensioning work is a considerably important link in the whole Engineering Drawing. For existing completeness testing of dimensioning,...
351
Authors: Li Gang Qu, Hai Jun Zhou, Zhi Liang Dong
Chapter 4: Communication and Information Technologies
Abstract:The information extraction mode based on machining feature was presented for studying information extraction of part process model in the 3D...
287