Using Normalized Interest Point Trajectories Over Scale for Image Search

Image search and object recognition are two domains where it is useful to be able to describe an image in a form that is invariant to image lighting, image intensity, scaling, rotation, translation, and changes in camera position. This paper presents a method based on tracing the trajectories of interest points, specifically KLT corners, across scale-space. The KLT corner interest points are calculated with an adaptive threshold to make them invariant to image intensity. A three-dimensional point composed of two-dimensional spatial coordinates and the scale of gaussian smoothing is found for each interest point, together all the points in the image are normalized into a form that is mostly invariant to geometric changes such as scale and rotation. Each image is converted to a trajectory set which is compared between images to assess their similarity. Experiments are shown.