Data-driven scene understanding by adaptive exemplar retrieval

This article studies a data-driven approach for semantically scene understanding, without pixelwise annotation and classifier pre-training. Our framework parses a target image with two steps: (i) retrieving its exemplars (i.e. references) from an image database, where all images are unsegmented but annotated with tags; (ii) recovering its pixel labels by propagating semantics from the references. We present a novel framework making the two steps mutually conditional and bootstrapped under the probabilistic Expectation-maximization (EM) formulation. In the first step, the references are selected by jointly matching their appearances with the target as well as the semantics. We process the second step via a combinatorial graphical representation, in which the vertices are superpixels extracted from the target and its selected references. Then we derive the potentials of assigning labels to one vertex of the target, which depends upon the graph edges that connect the vertex to its spatial neighbors of the target and to its similar vertices of the references. Two steps can be both solved analytically, and the inference is conducted in a self-driven fashion. In the experiments, we validate our approach on two public databases, and demonstrate superior performances over the state-of-the-art methods.