Image Parsing: Unifying Segmentation, Detection, and Recognition
In this chapter we present a Bayesian framework for parsing images into their constituent visual patterns. The parsing algorithm optimizes the posterior probability and outputs a scene representation as a "parsing graph", in a spirit similar to parsing sentences in speech and natural language. The algorithm constructs the parsing graph and re-configures it dynamically using a set of moves, which are mostly re-versible Markov chain jumps. This computational framework integrates two popular inference approaches - generative (top-down) methods and discriminative (bottom-up) methods. The former formulates the posterior probability in terms of generative models for images defined by likelihood functions and priors. The latter computes discriminative probabilities based on a sequence (cascade) of bottom-up tests/filters. In our Markov chain algorithm design, the posterior probability, defined by the generative models, is the invariant (target) probability for the Markov chain, and the discriminative probabilities are used to construct proposal probabilities to drive the Markov chain. Intuitively, the bottom-up discriminative probabilities activate top-down generative models. In this chapter, we focus on two types of visual patterns - generic visual patterns, such as texture and shading, and ob ject patterns including human faces and text. These types of patterns compete and cooperate to explain the image and so image parsing unifies image segmentation, ob ject detection, and recognition (if we use generic visual patterns only then image parsing will correspond to image segmentation .). We illustrate our algorithm on natural images of complex city scenes and show examples where image segmentation can be improved by allowing ob ject specific knowledge to disambiguate low-level segmentation cues, and conversely where ob ject detection can be improved by using generic visual patterns to explain away shadows and occlusions.