Global matching to enhance the strength of local intensity order pattern feature descriptor

Authors:
Hassan Dawood;Hussain Dawood;Ping Guo
Affiliations:
Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing, China;Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing, China;Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing, China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Year:
2013

Citing 22
Cited 0

Automated Inspection of Textile Fabrics Using Textural Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching Widely Separated Views Based on Affine Invariant Regions

International Journal of Computer Vision
Simple Gabor feature space for invariant object recognition

Pattern Recognition Letters
Scale & Affine Invariant Interest Point Detectors

International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Sparse Texture Representation Using Local Affine Regions

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Performance Evaluation of Local Descriptors

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparison of Affine Region Detectors

International Journal of Computer Vision
Scalable Recognition with a Vocabulary Tree

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Face Description with Local Binary Patterns: Application to Face Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Panoramic Image Stitching using Invariant Features

International Journal of Computer Vision
Description of interest regions with local binary patterns

Pattern Recognition
Rotation invariant texture classification using LBP variance (LBPV) with global matching

Pattern Recognition
Improvement of Image Modeling with Affinity Propagation Algorithm for Semantic Image Annotation

ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
DAISY: An Efficient Dense Descriptor Applied to Wide-Baseline Stereo

IEEE Transactions on Pattern Analysis and Machine Intelligence
WLD: A Robust Local Image Descriptor

IEEE Transactions on Pattern Analysis and Machine Intelligence
SURF: speeded up robust features

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Face authentication using adapted local binary pattern histograms

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Aggregating gradient distributions into intensity orders: A novel local image descriptor

CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Local Intensity Order Pattern for feature description

ICCV '11 Proceedings of the 2011 International Conference on Computer Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

Local intensity order pattern feature descriptor is proposed to extract the feature of image recently. However, it did not provide the global information of an image. In this paper, a simple, efficient and robust feature descriptor is presented, which is realized by adding the global information to local intensity features. A descriptor, which utilizes local intensity order pattern and/or global matching, is proposed to gather the global information with local intensity order. Experimental results shows that the proposed hybrid approach outperform over the state-of-the art feature extraction method like scale-invariant feature transform, local intensity order pattern and DAISY for standard oxford dataset.