Image Feature Extraction Using Gradient Local Auto-Correlations

Authors:
Takumi Kobayashi;Nobuyuki Otsu
Affiliations:
National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan;National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan
Venue:
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Year:
2008

Citing 11
Cited 9

The Design and Use of Steerable Filters

IEEE Transactions on Pattern Analysis and Machine Intelligence
The image processing handbook (2nd ed.)

The image processing handbook (2nd ed.)
Content-Based Image Retrieval at the End of the Early Years

IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Object Detection in Images by Components

IEEE Transactions on Pattern Analysis and Machine Intelligence
Space-time Interest Points

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Pedestrian Detection in Crowded Scenes

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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 Performance Evaluation of Local Descriptors

IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Irregularities in Images and in Video

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study

International Journal of Computer Vision

Image classification using probability higher-order local auto-correlations

ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Pedestrian recognition using second-order HOG feature

ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Feature extraction based on co-occurrence of adjacent local binary patterns

PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
Motion recognition using local auto-correlation of space-time gradients

Pattern Recognition Letters
Image matching based on orientation-magnitude histograms and global consistency

Pattern Recognition
Image representation for generic object recognition using higher-order local autocorrelation features on posterior probability images

Pattern Recognition
Efficient optimization for low-rank integrated bilinear classifiers

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Efficient similarity derived from kernel-based transition probability

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Personal feature extraction via grip force sensors mounted on a mobile phone: authenticating the user during key-operation

Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a method for extracting image features which utilizes 2nd order statistics, i.e., spatial and orientational auto-correlations of local gradients. It enables us to extract richer information from images and to obtain more discriminative power than standard histogram based methods. The image gradients are sparsely described in terms of magnitude and orientation. In addition, normal vectors on the image surface are derived from the gradients and these could also be utilized instead of the gradients. From a geometrical viewpoint, the method extracts information about not only the gradients but also the curvatures of the image surface. Experimental results for pedestrian detection and image patch matching demonstrate the effectiveness of the proposed method compared with other methods, such as HOG and SIFT.