Feature Extraction and Classification of Images Based on Corner Invariant Moments

Xue Ming Zhai; Dong Ya Zhang; Yu Jia Zhai; Ruo Chen Li; De Wen Wang

doi:10.4028/www.scientific.net/AMM.475-476.374

Paper Titles

The Trees Skeleton Extraction Based on Point Cloud Contraction
p.355

Detection of early Defects of Apple Based on the Image Fusion
p.361

Optimizing of Light Stripe Image Acquisition in Light Section Method Measurement
p.366

Extraction Method of PCA for Alteration Mineral Mapping, Using Landsat TM Data in Northwestern Junggar Basin
p.370

Feature Extraction and Classification of Images Based on Corner Invariant Moments
p.374

The Smarandachely Adjacent Vertex Distinguishing E-Total Coloring of some Join Graphs
p.379

An Efficient Imaging Strategy for Single Pixel Camera
p.383

Study on Characteristic Parameters of Speech Anti-Deliberate Imitation System
p.388

Adaptive Fourth Order Partial Differential Equation Filter from the Webers Total Variation for Image Restoration
p.394

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 475-476Feature Extraction and Classification of Images...

Feature Extraction and Classification of Images Based on Corner Invariant Moments

Abstract:

Image feature extraction and classification is increasingly important in all sectors of the images system management. Aiming at the problems that applying Hu invariant moments to extract image feature computes large and too dimensions, this paper presented Harris corner invariant moments algorithm. This algorithm only calculates corner coordinates, so can reduce the corner matching dimensions. Combined with the SVM (Support Vector Machine) classification method, we conducted a classification for a large number of images, and the result shows that using this algorithm to extract invariant moments and classifying can achieve better classification accuracy.

You might also be interested in these eBooks

Sensors, Measurement and Intelligent Materials II

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 475-476)

Pages:

374-378

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.475-476.374

Citation:

Cite this paper

Online since:

December 2013

Authors:

Xue Ming Zhai*, Dong Ya Zhang, Yu Jia Zhai, Ruo Chen Li, De Wen Wang

Keywords:

Classification, Feature Extraction, Harris Corner, Hu Invariant Moments

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] L. D, Y. P, Information Management System of Substation Equipment based on image, Telecommunications for Electric Power System, 2007, 28(174): 61-65.

Google Scholar

[2] B.P. A Comparison of Approaches for Image Classification,. Modern Electric Technology, (2009).

Google Scholar

[3] HU M K．Visual pattern recognition by moment invariant．IEEE Transactions on Information Theory. 1962, 8(2): 179-187.

Google Scholar

[4] L. D, Research and Implementation of Electric Power Equipment Pictures Management System, 2006, North China Electric Power University.

Google Scholar

[5] K. X,Z. W, Classification and identification of mechanical parts based on image invariant moments and SVM,. Manufacturing Automation, 2012, 34(8): 65-69.

Google Scholar

[6] Harris C G. A combined corner and edge detector. 4th Alvey Vision Conference, (1988).

Google Scholar

[7] Vapnik V N. The nature of statistical learning theory. New York: Springer-Verlag, (1995).

Google Scholar