Figure 5.

Canonical architecture implemented by various popular object recognition methods. Images are represented by sets of descriptors. A set of representative descriptors {⁽²⁾_c} is learned from an image training set. The descriptors y⁽¹⁾(l) extracted from the image to classify are then encoded, with respect to this set of representatives. The encoding consists of assigning each descriptor to a subset of the representatives, using a similarity function s(y⁽¹⁾(l), ⁽²⁾_c). This could be a probabilistic function, e.g., probability under a Gaussian mixture model, or a sparse encoding. The assignments are finally pooled spatially to produce assignment histograms, which are fed to a classifier, e.g., a support vector machine.