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Shape context is a feature descriptor used in object recognition. Serge Belongie and Jitendra Malik proposed the term in their paper "Matching with Shape Contexts" in 2000.〔 〕 ==Theory== The shape context is intended to be a way of describing shapes that allows for measuring shape similarity and the recovering of point correspondences.〔 The basic idea is to pick ''n'' points on the contours of a shape. For each point ''p''''i'' on the shape, consider the ''n'' − 1 vectors obtained by connecting ''p''''i'' to all other points. The set of all these vectors is a rich description of the shape localized at that point but is far too detailed. The key idea is that the distribution over relative positions is a robust, compact, and highly discriminative descriptor. So, for the point ''p''''i'', the coarse histogram of the relative coordinates of the remaining ''n'' − 1 points, : is defined to be the shape context of . The bins are normally taken to be uniform in log-polar space. The fact that the shape context is a rich and discriminative descriptor can be seen in the figure below, in which the shape contexts of two different versions of the letter "A" are shown. (a) and (b) are the sampled edge points of the two shapes. (c) is the diagram of the log-polar bins used to compute the shape context. (d) is the shape context for the point marked with a circle in (a), (e) is that for the point marked as a diamond in (b), and (f) is that for the triangle. As can be seen, since (d) and (e) are the shape contexts for two closely related points, they are quite similar, while the shape context in (f) is very different. Now in order for a feature descriptor to be useful, it needs to have certain invariances. In particular it needs to be invariant to translation, scale, small perturbations, and depending on application rotation. Translational invariance come naturally to shape context. Scale invariance is obtained by normalizing all radial distances by the mean distance between all the point pairs in the shape 〔 〕〔 〕 although the median distance can also be used.〔〔 〕 Shape contexts are empirically demonstrated to be robust to deformations, noise, and outliers〔 using synthetic point set matching experiments.〔 〕 One can provide complete rotation invariance in shape contexts. One way is to measure angles at each point relative to the direction of the tangent at that point (since the points are chosen on edges). This results in a completely rotationally invariant descriptor. But of course this is not always desired since some local features lose their discriminative power if not measured relative to the same frame. Many applications in fact forbid rotation invariance e.g. distinguishing a "6" from a "9". 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「shape context」の詳細全文を読む スポンサード リンク
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