SIFT Feature Extraction Based on Color Energy Region

Jun Zhang; Lin Zhang; An Quan Sun; Shu Bing Li; Xiao Bing Tang; Hong Wang

doi:10.4028/www.scientific.net/AMM.411-414.1201

Paper Titles

JPEG Steganalysis Based on Locality Preserving Projection Dimensionality Reduction Method
p.1185

A New Adaptive Image Fusion Technique of CT and MRI Images Based on Dual-Tree Complex Wavelet Transform
p.1189

Grayscale Image Compression and Encryption Based on Format Conversion
p.1193

A Semantic Watermarking Technique for Authenticating Video of H.264
p.1197

SIFT Feature Extraction Based on Color Energy Region
p.1201

A Study Based on Image Compression Technology Using Wavelet Transform
p.1205

Application of Chaotic Neural Network and 3D DWT-DFT in Medical Volume Data Watermarking
p.1209

The Study and Implementation of Digital Camera Positioning Based on Pinhole Model
p.1213

Fault Automatic Detection Method of Steel Cord Conveyor Belt Based on Gabor Filter Bank
p.1218

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 411-414SIFT Feature Extraction Based on Color Energy...

SIFT Feature Extraction Based on Color Energy Region

Abstract:

In content-based image feature extraction research areas, SIFT feature occupies a very important position. In 2004 it was first proposed, widely used in object recognition, video tracking, scene recognition, image retrieval and other issues, and achieved great success. But the extraction of image SIFT features needs a huge amount of computation. This paper presents the concept of color energy in where it has great information, and extract large color energy regions, extracted SIFT feature points in them. Although losing some of the feature points, this method effectively reduces the computational complexity, and reduces the computation time.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 411-414)

Pages:

1201-1204

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.411-414.1201

Citation:

Cite this paper

Online since:

September 2013

Authors:

Jun Zhang, Lin Zhang, An Quan Sun, Shu Bing Li, Xiao Bing Tang, Hong Wang

Keywords:

Color Energy, Color Quantization, Histogram, Scale Invariant FeatureTransform (SIFT) Features

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] D.G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision(2004).

DOI: 10.1023/b:visi.0000029664.99615.94

Google Scholar

[2] Jinle Gao, Yaohong Kang, Xiaoqin Wu. Research on image retrieval based on color feature[J]. Information Technology, (2008), 11: 0004-0007. In Chinese.

Google Scholar

[3] Y. Ke and R. Sukthankar. PCA-SIFT: A more Distinctive Representation for Local Image Descriptors, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2004).

DOI: 10.1109/cvpr.2004.1315206

Google Scholar

[4] S. Belongie,J. Malik, and J. Puzicha, Shape Matching and Object Recognition Using Shape Contexts, IEEE Transaction on Pattern Analysis and Machine Intelligence, (2002).

DOI: 10.1109/34.993558

Google Scholar

[5] Liangshen Wang, Zongying Ou, Jie Hou, etc. Retrieval to image database based on color features with pyramid construction[J]. Computer Engineering and Design. (2005), 26(4): 1041-1042, 1047. In Chinese.

Google Scholar

[6] Yaben He, Xiaoping Ji. Image Retrieval Based on Nine Blocks and Color Features[J]. Software. (2011), 32(11): 29-31. In Chinese.

Google Scholar

[7] Junding Sun, Shan Zhao. Low-level image feature extraction and retrieval technology [M]. Beijing: Electronic Industry Press. In Chinese(2009).

Google Scholar