Paper Info
Title
Rotation-invariant histograms of oriented gradients for local patch robust representation.
Abstract
Our research focuses on the question of feature descriptors for robust effective computing, presenting a novel feature representation method-rotation-invariant histograms of oriented gradients (Ri-HOG). Most of the existing HOG techniques are computed on a dense grid of uniformly-spaced cells and use overlapping local contrast of rectangular blocks for normalization. However, we adopt annular spatial bins type cells and apply radial gradient transform (RGT) to attain gradient binning invariance for feature descriptors. In such way, it significantly enhances HOG with respect to rotation-invariant ability and feature descripting accuracy. In experiments, the proposed method adopts object recognition as a test case and it is evaluated on PASCAL VOC 2007 dataset. The experimental results demonstrate that the proposed method is much more efficient than the existing methods.
Year
Venue
Field
2015
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference
Histogram,Normalization (statistics),Pattern recognition,Feature (computer vision),Robustness (computer science),Feature extraction,Invariant (mathematics),Artificial intelligence,Grid,Mathematics,Cognitive neuroscience of visual object recognition
DocType
ISSN
Citations 
Conference
2309-9402
0
PageRank 
References 
Authors
0.34
8
4
Name
Order
Citations
PageRank
Zhaojie Luo111.39
J. Chen211223.18
Tetsuya Takiguchi330852.22
Yasuo Ariki451988.94