scholarly journals Lemp: a Robust Image Feature Descriptor for Retrieval Applications

2019 ◽  
Vol 9 (1) ◽  
pp. 4565-4569
Keyword(s):  
Local Region ◽  
Image Feature ◽  
Feature Descriptor ◽  
Robust Performance ◽  
Edge Information ◽  
Retrieval Rate ◽  
Edge Distribution ◽  
Robust Image ◽  
Experimental Values

line edge magnitude pattern (lemp) is proposed in this paper. Line edge distribution is used to denote local region of an image. Popular texture descriptors such as lbp deal with a comparison of centre pixel with neighbors and thus encode the information. In lemp ,pixel at the centre is replaced by edge values of neighbors. Discriminating information provided by line edges makes this method different from many of the existing methods. Magnitude is also added to the line edge information in order to make the feature descriptor more effective and robust. Performance of lemp method is estimated with corel database. Standard metrics such as recall, precision and average retrieval rate are determined for comparison purpose. Experimental values exhibit a notable improvement in the performance.

scholarly journals A New Retrieval Algorithm Based on Pulse Coupled Neural Network for Biomedical Images

2019 ◽  
Vol 9 (1S) ◽  
pp. 9-13
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Rapid Expansion ◽  
Image Feature ◽  
Experimental Result ◽  
Feature Descriptor ◽  
Resonance Imaging ◽  
Biomedical Images ◽  
Pulse Coupled Neural Network

The rapid expansion and improvement in medical science and technology lead to the generation of more image data in its regular activity such as computed tomography (CT), X-ray, magnetic resonance imaging (MRI) etc. To manage the medical images properly for clinical decision making, content-based medical image retrieval (CBMIR) system emerged. In this paper, Pulse Coupled Neural Network (PCNN) based feature descriptor is proposed for retrieval of biomedical images. Time series is used as an image feature which contains the entire information of the feature, based on which the similar biomedical images are retrieved in our work. Here, the physician can point out the disorder present in the patient report by retrieving the most similar report from related reference reports. Open Access Series of Imaging Studies (OASIS) magnetic resonance imaging dataset is used for the evaluation of the proposed approach. The experimental result of the proposed system shows that the retrieval efficiency is better than the other existing systems.

Orthogonal Low-rank Projection Learning for Robust Image Feature Extraction

2021 ◽  
pp. 1-1
Author(s):  
Xiaoqian Zhang ◽  
Zhen Tan ◽  
Huaijiang Sun ◽  
Zungang Wang ◽  
Mingwei Qin
Keyword(s):  
Feature Extraction ◽  
Image Feature ◽  
Low Rank ◽  
Image Feature Extraction ◽  
Robust Image

4FP-Structure: A Robust Local Region Feature Descriptor

2017 ◽  
Vol 83 (12) ◽  
pp. 813-826 ◽  
Author(s):  
Jiayuan Li ◽  
Qingwu Hu ◽  
Mingyao Ai
Keyword(s):  
Local Region ◽  
Feature Descriptor ◽  
Region Feature

Image feature descriptor based on shape salience points

2013 ◽  
Vol 120 ◽  
pp. 156-163 ◽  
Author(s):  
Glauco V. Pedrosa ◽  
Marcos A. Batista ◽  
Celia A.Z. Barcelos
Keyword(s):  
Image Feature ◽  
Feature Descriptor

A Novel Hybrid Discrete Cosine Transform Speeded Up Robust Feature-Based Secure Medical Image Watermarking Algorithm

2020 ◽  
Vol 10 (11) ◽  
pp. 2588-2599
Author(s):  
Saqib Ali Nawaz ◽  
Jingbing Li ◽  
Uzair Aslam Bhatti ◽  
Anum Mehmood ◽  
Raza Ahmed ◽  
...  
Keyword(s):  
Discrete Cosine Transform ◽  
Digital Watermarking ◽  
Copyright Protection ◽  
Image Watermarking ◽  
Image Feature ◽  
Feature Descriptor ◽  
Cosine Transform ◽  
Speeded Up Robust Features ◽  
Feature Based ◽  
Dct Coefficients

With the advancement of networks and multimedia, digital watermarking technology has received worldwide attention as an effective method of copyright protection. Improving the anti-geometric attack ability of digital watermarking algorithms using image feature-based algorithms have received extensive attention. This paper proposes a novel robust watermarking algorithm based on SURF-DCT perceptual hashing (Speeded Up Robust Features and Discrete Cosine Transform), namely blind watermarking. We design and implement a meaningful binary watermark embedding and extraction algorithm based on the SURF feature descriptor and discrete-cosine transform domain digital image watermarking algorithm. The algorithm firstly uses the affine transformation with a feature matrix and chaotic encryption technology to preprocess the watermark image, enhance the confidentiality of the watermark, and perform block and DCT coefficients extraction on the carrier image, and then uses the positive and negative quantization rules to modify the DCT coefficients. The embedding of the watermark is completed, and the blind extraction of the watermark realized. Correlation values are more than 90% in most of the attacks. It provides better results against different noise attacks and also better performance against rotation. Transparency and high computational efficiency, coupled with dual functions of copyright protection and content authentication, is the advantage of the proposed algorithm.

Low-Rank Embedding for Robust Image Feature Extraction

2017 ◽  
Vol 26 (6) ◽  
pp. 2905-2917 ◽  
Author(s):  
Wai Keung Wong ◽  
Zhihui Lai ◽  
Jiajun Wen ◽  
Xiaozhao Fang ◽  
Yuwu Lu
Keyword(s):  
Feature Extraction ◽  
Image Feature ◽  
Low Rank ◽  
Image Feature Extraction ◽  
Robust Image

Exact order based feature descriptor for illumination robust image matching

2013 ◽  
Vol 46 (12) ◽  
pp. 3268-3278 ◽  
Author(s):  
Bongjoe Kim ◽  
Hunjae Yoo ◽  
Kwanghoon Sohn
Keyword(s):  
Image Matching ◽  
Feature Descriptor ◽  
Exact Order ◽  
Robust Image
Sign in / Sign up

Export Citation Format

Share Document