HIMO: Cross-Arbitrary-Modality Image Invariant Feature Transform with Hierarchical Intrinsic Major Orientation

Invariant feature extraction is a critical challenge in intelligent image processing, particularly with the rapid advancement of multi-source/modal imaging. Cross-modal matching has attracted considerable attention, yet current studies primarily focus on targeted modalities rather than realizing a general approach. In this paper, cross-arbitrary-modal image invariant feature extraction and matching is studied. Inspired by human vision, a purely handcrafted invariant feature transform is proposed for universal cross-modal image matching, named Hierarchical Intrinsic Major Orientation (HIMO). Based on orientation information, a full-chain non-data-driven algorithm is designed that hinges on an Intrinsic Major Orientation (IMO) extraction. The HIMO incorporates a novel keypoint detector utilizing Difference-of-Feature Suppression (DoFS), a Polar-Pyramid descriptor (PolarP), and a Cascaded Dynamic Multi-scale Strategy (CDMS) to effectively address common challenges such as intensity distortion, rotation, scale differences, geometric deformation, and image noise. To validate the proposed method, two massive cross-modal datasets-General Cross-modal Zone (GCZ) and Wide-area Diverse Sources (WDS)-are introduced, alongside two practical evaluation metrics. Comprehensive experiments compared with 10 traditional and 15 deep-learning state-of-the-art algorithms on 5 datasets fully demonstrate that the proposed HIMO achieves superior performance in terms of robustness, stability, and generalization across diverse imaging conditions.