Content-based image retrieval by scale-space object boundary shape representation

Shape is a popular feature used for content-based image retrieval. In this paper we propose a new method for image retrieval using a shape boundary represented in scale-space. The proposed method is suggested by the notion of `dynamic shape` where all 2-D boundary representations evolve from a single, primeval, featureless shape - a circle. Shape is represented by linearizing the boundary based on the polar coordinates of boundary points relative to the object's centroid. Points on the shape boundary are mapped to a primeval circle, and two functions are defined, the Radius Difference Function and the Angle Difference Function, and smoothed through scale-space to devolve the shape. Maxima and minima of the Radius Difference Function are extracted and used to calculate similarity between objects. Similarity is calculated using Euclidean distance. Other scale-space approaches to shape representation use various techniques to maintain constant boundary arc length, that may otherwise change in non-intuitive ways over scale. We introduce the contour stability over scale property stating that the perceived boundary length should not change significantly over scale. Experiments show that significant similarity computation may be saved by using coarser scales without effectively reducing retrieval performance.