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Related Experiment Videos

Bottom-up/top-down image parsing with attribute grammar.

Feng Han1, Song-Chun Zhu

  • 1Sarnoff Corporation, Princeton, NJ 08543, USA. hanf@cs.ucla.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 26, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a graph grammar for generating man-made scenes and an inference algorithm for image parsing. The method significantly enhances bottom-up detection performance using grammar rules and spatial constraints.

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Computational Geometry

Background:

  • Scene understanding is crucial for AI applications.
  • Existing methods struggle with complex scene parsing and occlusions.
  • Generative models offer a promising approach for scene representation.

Purpose of the Study:

  • To propose a novel attribute graph grammar for representing man-made scenes.
  • To develop an effective top-down/bottom-up inference algorithm for image parsing.
  • To improve the performance of bottom-up detection using grammatical and top-down inference.

Main Methods:

  • A generative attribute graph grammar is used for scene representation.
  • A hybrid top-down/bottom-up inference algorithm maximizes Bayesian posterior probability (or minimizes Minimum Description Length).
  • The algorithm constructs a parse graph including a parse tree and spatial constraints.

Main Results:

  • The bottom-up step identifies numerous weighted rectangular candidates.
  • Top-down predictions are activated for occluded or missing components via grammar rules.
  • The proposed grammar and inference significantly improve bottom-up detection accuracy.

Conclusions:

  • Attribute graph grammars provide an effective generative representation for man-made scenes.
  • The top-down/bottom-up inference algorithm successfully parses images and handles occlusions.
  • This approach enhances scene understanding by integrating generative models with robust inference techniques.