We present a computationally efficient, on-line graph
structure estimation method for model-based scene interpretation.
Different scenes have different hierarchical
graphical models composed of place, objects, and parts.
Generally, it is very difficult and time-consuming to estimate
dynamic graph structures. The key idea is to represent
hypothesized graph structures as multi-modal particles instead
of joint particle representation. Such Monte Carlo
representation makes the one-line hierarchical graph structure
estimation feasible. The proposed method is supported
by the neurobiological inference model. Large-scale experimental
results in an indoor (12 places, 112 3D objects)
validate the feasibility of the proposed inference method.
structure estimation method for model-based scene interpretation.
Different scenes have different hierarchical
graphical models composed of place, objects, and parts.
Generally, it is very difficult and time-consuming to estimate
dynamic graph structures. The key idea is to represent
hypothesized graph structures as multi-modal particles instead
of joint particle representation. Such Monte Carlo
representation makes the one-line hierarchical graph structure
estimation feasible. The proposed method is supported
by the neurobiological inference model. Large-scale experimental
results in an indoor (12 places, 112 3D objects)
validate the feasibility of the proposed inference method.