.png){kind=icon}
.png){kind=icon}
Return
Content-Based Image Retrieval Using Angles Across Scales
.png){kind=icon}
PRE
AI
DOI：10.1109/LGRS.2021.3131340.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This letter proposes a content-based image retrieval technique using novel dense angle descriptor and dictionary learning (DL). The histogram of oriented gradients (HOG) descriptor fails to obtain rotation invariance and well-defined rotation behavior, and therefore, a dense angle-based HOG descriptor has been presented to address the image rotation invariance. The technique computes angles across multiple scales and uses bag-of-visual features at different scales for DL. Experiments conducted on building and remote sensing datasets show that the proposed technique achieves high retrieval performance.
Keywords:
Image retrieval
Visualization
Remote sensing
Dictionaries
Histograms
Feature extraction
Convolutional neural networks
Angle descriptor
dictionary learning (DL)
image retrieval
Journal
IF:
16.4
Papers:
1.0W
Citations:
5.1K