Content-based Image Retrieval Using Rotation-invariant Histograms of Oriented Gradients
Jinhui Chen, Toru Nakashika, Tetsuya Takiguchi, Yasuo Ariki · 2015
Our research focuses on the question of feature descriptors for robust effective computing, proposing a novel feature representation method, namely, rotation-invariant histograms of oriented gradients (Ri-HOG) for image retrieval. 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 to attain gradient binning invariance for feature extraction. In this way, it significantly enhances HOG in regard to rotation-invariant ability and feature descripting accuracy. In experiments, the proposed method is evaluated on Corel-5k and Corel-10k datasets. The experimental results demonstrate that the proposed method is much more effective than many existing image feature descriptors for content-based image retrieval.