Key Sample Point Selection: An Improvement of Shape Context Algorithm in Image Retrieval

A. Nodari, E. Binaghi, M. Carullo, and I. Gallo (Italy)


Shape Context, Content-based Retrieval, Pattern Recognition.


In this work we defined a new algorithm in the field of Content Based Image Retrieval. The Shape Context Algorithm presents a promising solution to the Shape Analysis problem however its use is strongly limited by the high demand of time and space due to the elevated number of Sample Points required. The new algorithm proposed in this study aims to improve the original Shape Context algorithm’s performance modifying some its relevant parts; furthermore, it was evaluated in term of accuracy, computational time and space. The salient aspects of our algorithm are: a new strategy for the Sample Points selection and a center of mass angle approximation technique in the phase of the shape description computation. We want to reduce the number of Sample Points required by the original algorithm in order to attempt to improve the efficiency in real applications.

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