Matching and retrieval of distorted and occluded shapes using dynamic programming
We propose an approach for matching distorted and possibly occluded shapes using Dynamic Programming (DP). We distinguish among various cases of matching such as cases where the shapes are scaled with respect to each other and cases where an open shape matches the whole or only a part of another open or closed shape. Our algorithm treats noise and shape distortions by allowing matching of merged sequences of consecutive small segments in a shape with larger segments of another shape, while being invariant to translation, scale, orientation, and starting point selection. We illustrate the effectiveness of our algorithm in retrieval of shapes on two data sets of two-dimensional open and closed shapes of marine life species. We demonstrate the superiority of our approach over traditional approaches to shape matching and retrieval based on Fourier descriptors and moments. We also compare our method with SQUID, a well-known method which is available on the Internet. Our evaluation is based on human relevance judgments following a well-established methodology from the information retrieval field.
dynamic programming relevance judgments image database shape retrieval query by example