Grammar-based object representations in a scene parsing task

Grammar-based Object Representations in a Scene Parsing Task - Savova, Jäkel, Tenenbaum savova:2009 Computational Intelligence E, SS 2010

Scene Parsing Using Scene Attributes As Global Features

Data-driven methods have been proven very effective for the task of scene parsing. A crucial step in these methods is to retrieve a set of visually similar scenes from existing image collections for the query image according to certain global scene representations. In this work, we incorporate scene attributes into data-driven scene parsing systems as global scene features. We show that when us...

Integrating Function, Geometry, Appearance for Scene Parsing

In this paper, we present a Stochastic Scene Grammar (SSG) for parsing 2D indoor images into 3D scene layouts. Our grammar model integrates object functionality, 3D object geometry, and their 2D image appearance in a Function-Geometry-Appearance (FGA) hierarchy. In contrast to the prevailing approach in the literature which recognizes scenes and detects objects through appearance-based classifi...

Image Parsing via Stochastic Scene Grammar

This paper proposes a parsing algorithm for scene understanding which includes four aspects: computing 3D scene layout, detecting 3D objects (e.g. furniture), detecting 2D faces (windows, doors etc.), and segmenting background. In contrast to previous scene labeling work that applied discriminative classifiers to pixels (or super-pixels), we use a generative Stochastic Scene Grammar (SSG). This...

3D Scene Grammar for Parsing RGB-D Pointclouds

We pose 3D scene-understanding as a problem of parsing in a grammar. A grammar helps us capture the compositional structure of real-word objects, e.g., a chair is composed of a seat, a back-rest and some legs. Having multiple rules for an object helps us capture structural variations in objects, e.g., a chair can optionally also have arm-rests. Finally, having rules to capture composition at di...