Perceptually Motivated Shape Context Which Uses Shape Interiors

In this paper, we identify some of the limitations of current-day shape matching techniques. We provide examples of how contour-based shape matching techniques cannot provide a good match for certain visually similar shapes. To overcome this limitation, we propose a perceptually motivated variant of the well-known shape context descriptor. We identify that the interior properties of the shape play an important role in object recognition and develop a descriptor that captures these interior properties. We show that our method can easily be augmented with any other shape matching algorithm. We also show from our experiments that the use of our descriptor can significantly improve the retrieval rates

Two perceptually motivated strategies for shape classification

In this paper, we propose two new, perceptually motivated strategies to better measure the similarity of 2D shape instances that are in the form of closed contours. The first strategy handles shapes that can be decomposed into a base structure and a set of inward or outward pointing “strand” structures, where a strand structure represents a very thin, elongated shape part attached to the base structure. The similarity of two such shape contours can be better described by measuring the similarity of their base structures and strand structures in different ways. The second strategy handles shapes that exhibit good bilateral symmetry. In many cases, such shapes are invariant to a certain level of scaling transformation along their symmetry axis. In our experiments, we show that these two strategies can be integrated into available shape matching methods to improve the performance of shape classification on several widelyused shape data sets. Shape matching through nonrigid shape deformation is a typical approach to measure shape similarity [6, 14, 10, 21, 13, 16]. In general, this approach measures the amount of energy required to deform one shape contour into another based on some physical or mathematical model. The model is then optimized using methods such as dynamic programming to obtain a set of corresponded points on the two shape contours that minimize the deformation cost of this model. However, this approach is often very sensitive to strong, local shape variations that human vision may handle very well. For example, the two shape contours shown in Figs. 1(a) and (b) are similar in general, but their outward parts, represented by the dashed curves, are quite different from each other. A large deformation cost may be required to match these two shape contours. 1