Content-Based Image Retrieval Using Angles Across Scales

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.

Automated summary

This letter introduces a content-based image retrieval technique using a novel dense angle descriptor and dictionary learning. It addresses the issue of rotation invariance in image retrieval by presenting a dense angle-based HOG descriptor. Experimental results on different datasets demonstrate that the proposed technique achieves high retrieval performance.