Reduced SIFT Features For Image Retrieval and Indoor Localisation

SIFT features are distinctive invariant features used to robustly describe and match digital image content between different views of a scene. While invariant to scale and rotation, and robust to other image transforms, the SIFT feature description of an image is typically large and slow to compute. This paper presents a method to reduce the size, complexity and matching time of SIFT feature sets for use in indoor image retrieval and robot localisation. Our method takes advantage of the structure of typical indoor environments to reduce the complexity of each SIFT feature and the number of SIFT features required to describe a scene. Our results show that there is a minimal loss of accuracy in feature retrieval while achieving a significant reduction in image descriptor size and matching time. We also outline how the scale information of the SIFT features can be used to improve the accuracy of a localisation filter. The results were obtained using digital images from interior home and office environments.