Rotation invariant feature descriptor integrating HAVA and RIFT

Local feature descriptors, which are distinctive and yet invariant to many kinds of geometric and photometric transformations, have been paid more and more research attentions due to their promising performance. Aiming at tackling difficulties in the estimation of local dominant orientation and high dimensionality of the state-of-the-art local feature descriptors, a novel rotation invariant descriptor HAVA-RIFT (Histogram of Absolute Value Activity-Rotation Invariant Feature Transform) is proposed. Firstly, Harris-Laplace detector is utilized to obtain the candidate multi-scale corners and corresponding characteristic scales. Secondly, histograms of absolute value activity and rotation invariant feature transform descriptor are computed in the local region. Finally, a two-step double-threshold matching strategy is applied to determine the matching relationship and the two-way matching principle is used to eliminate the mismatches of "many-to-one". Experiments on real images have demonstrated that HAVA-RIFT descriptor outperforms the existing RIFT descriptor under various conditions such as scaling, rotation, light change, image blurring, affine transformation and JPEG compression.