Adaptive Multi-Scale Spatiogram (AMS): A Robust and Efficient Descriptor for Complex Visual Environments

In this paper, we introduce Adaptive Multi-Scale Spatiogram (AMS), a new feature descriptor that aims to enhance both robustness and accuracy in feature matching, under various transformations such as viewpoint, scale, rotation, and illumination changes. The proposed method dynamically adapts to local image features by capturing complex details in texture-rich areas while simplifying representation in smoother regions. The method combines a Bayesian model for detecting keypoints, adaptive binning based on entropy, and a low-rank approximation to handle a wide variety of scene changes. Experimental results demonstrate that the proposed method outperforms traditional descriptors in complex transformations, though with a moderate computational cost. The overall results suggest that AMS achieves a satisfactory balance between effectiveness and efficiency, making it suitable for advanced computer vision applications.