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.
Due to the diversity involved in shape-matching, it has become difficult to come up with a single measure that incorporates all the requirements. While the use of Euclidean distance is beneficial for identifying certain classes of objects, the use of inner distance favours some others. As a result, researchers have started to fuse two or more techniques while calculating the distance between two shapes. @cite_37 identified that the use of inner distance was overkill" for certain classes of objects and proposed a technique to balance deformability and discriminability. They calculate the cost between two shapes with the help of various distance measures, parameterised by an aspect weight, and retain the best" cost. However, they still use points sampled from the contour and their algorithm would therefore be susceptible to objects with strong base structures that have indentations in their contours.
@cite_37
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We propose a novel framework, aspect space, to balance deformability and discriminability, which are often two competing factors in shape and image representations. In this framework, an object is embedded as a surface in a higher dimensional space with a parameter named aspect weight, which controls the importance of intensity in the embedding. We show that this framework naturally unifies existing important shape and image representations by adjusting the aspect weight and the embedding. More importantly, we find that the aspect weight implicitly controls the degree to which a representation handles deformation. Based on this idea, we present the aspect shape context, which extends shape context-based descriptors and adaptively selects the "best" aspect weight for shape comparison. Another observation we have is the proposed descriptor nicely fits context-sensitive shape retrieval. The proposed methods are evaluated on two public datasets, MPEG7-CE-Shape-1 and Tari 1000, in comparison to state-of-the-art solutions. In the standard shape retrieval experiment using the MPEG7 CE-Shape-1 database, the new descriptor with context information achieves a bull's eye score of 95.96 , which surpassed all previous results. In the Tari 1000 dataset, our methods significantly outperform previous tested methods as well.