Image feature extraction algorithm in big data environment

2020 ◽  
Vol 39 (4) ◽  
pp. 5109-5118
Author(s):  
Yubao Zhang
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Image Matching ◽  
Image Database ◽  
Scale Space ◽  
Image Feature ◽  
Aerial Image ◽  
Image Feature Extraction ◽  
Matching Algorithm ◽  
Improved Algorithm

The purpose of this article is to explore effective image feature extraction algorithms in the context of big data, and to mine their potential information from complex image data. Based on the BRISK and SIFT algorithms, this paper proposes an image feature extraction and matching algorithm based on BRISK corner points. By combining the SIFT scale space and the BRISK algorithm, a new scale space construction method is proposed. The BRISK algorithm extracts the corner invariant features. Then, by using the improved feature matching method and eliminating the mismatching algorithm, the exact matching of the images is realized. A large number of experimental verifications were performed in the standard test Mikolajczyk image database and aerial image database. The experimental results show that the improved algorithm in this paper is an effective image matching algorithm. The highest accuracy of actual aerial image matching can reach 85.19%, and it can realize the actual aerial image matching that BRISK and SIFT algorithms cannot complete. The improved algorithm in this paper has the advantages of higher matching accuracy and strong robustness.

scholarly journals SIFT Feature Extraction Algorithm for Image in DCT Domain

2013 ◽  
Vol 347-350 ◽  
pp. 2963-2967 ◽  
Author(s):  
Zhen Wu ◽  
Zhe Xu ◽  
Rui Nian Zhang ◽  
Shao Mei Li
Keyword(s):  
Feature Extraction ◽  
Image Matching ◽  
Coefficient Matrix ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Sift Feature ◽  
Dct Domain ◽  
Extraction Algorithm ◽  
Dct Coefficients ◽  
The Relationship

Image feature extraction is an important technology in image matching and retrieval. For the problem of high computational complexity of spatial domain image feature extraction using the SIFT algorithm, and by studying the relationship between DCT coefficient matrix and image, the paper designed the DCT coefficients reduced matrix of image and proposed the algorithm of SIFT feature extraction in DCT domain reduced image. Experiments showed that with the low loss of accuracy in image matching and retrieval, the method proposed can significantly improve the computational efficiency of feature extraction.

Research on the SIFT Algorithm in Image Matching

2011 ◽  
Vol 121-126 ◽  
pp. 4656-4660 ◽  
Author(s):  
Yuan Cong ◽  
Xiao Rong Chen ◽  
Yi Ting Li
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Scale Space ◽  
Feature Point ◽  
Image Feature ◽  
Sift Algorithm ◽  
Matching Algorithm ◽  
Key Points ◽  
Point Neighborhood ◽  
Accurate Matching

SIFT feature matching algorithm is hot in the field of the currently feature matching research, its matching with the strong ability can deal with the translation, rotation, affine transformation occurring between images , and it also have a stable image feature matching ability to the images filmed at any angle. SIFT algorithm is adopted in this paper, matching feature point through the scale space , calculating the histogram of detecting feature point neighborhood of gradient direction characteristic vector and generation to SIFT eigenvector and the key points similarity measure. From different setting threshold, scale scaling, rotating, noise on the experiment, the experiment result proves this algorithm in the above aspects has good robustness, suitable for mass characteristic database of rapid, accurate matching.

scholarly journals Development of Speed Up Robust Feature Algorithm for aerial image feature extraction

2019 ◽  
Vol 11 (4) ◽  
pp. 49-60
Author(s):  
Saikat BANERJEE ◽  
Sudhir Kumar CHATURVEDI ◽  
Surya Prakash TIWARI
Keyword(s):  
Feature Extraction ◽  
Aerial Imagery ◽  
Image Feature ◽  
Aerial Image ◽  
Feature Points ◽  
Image Feature Extraction ◽  
Surface Models ◽  
Speed Up ◽  
Fine Structures ◽  
Speed Up Robust Feature

Speed Up Robust Feature Algorithm (SURF) has been a very useful technique in the advancement of image feature algorithm. The strategy offers an extremely decent agreement between the runtime and accuracy, especially at object borders and fine structures. It has a wide scope of applications in remote sensing like getting computerized surface models from UAV and satellite images. In this paper, SURF algorithm has been discussed in details to enhance the capability of the system for image feature extraction technique to detect and obtain the maximum feature points from aerial imagery. The algorithms are developed depending upon such phenomena in which a maximum result can be obtained in very less time.

Sign in / Sign up

Export Citation Format

Share Document