Some notes related to contour grouping and matching

The problem of computational perceptual grouping received considerable attention before the advent of appearance-based recognition, when object models were typically shape-based and image features were typically contour-based.[1]

Moreover, while object databases were rather small, it was generally assumed that a linear search of a database, i.e., matching the image features against each model in succession and choosing the best-matching model, was an unacceptable strategy, for it did not scale to very large databases. In an effort to achieve sublinear scaling, much effort was devoted to the problem of object indexing, i.e., using a set of image features to query the database for candidate objects that might account for the image features.[1]

拿模型一个一个去匹配的方法已经过时了，因为现在的图像库都很大，计算上不现实了。现在主要是研究如何更好的进行object indexing。

grouping was based not on object-level prior knowledge, but rather on mid-level (object-independent) prior knowledge. Such grouping was essential, since local contour features were highly ambiguous, and without grouping them into more discriminative structures, effective indexing into large databases was problematic.[1]

Grouping所组织出来的是mid level的信息，是有用的。

References:

[1] Sala, P. & Dickinson, S. J. Contour Grouping and Abstraction Using Simple Part Models European Conference on Computer Vision, 2010, 603-616