Compositional Boosting for Computing Hierarchical Image Structures

In this paper, we present a compositional boosting algorithm for detecting and recognizing 17 common image structures in low-middle level vision tasks. These structures, called "graphlets", are the most frequently occurring primitives, junctions and composite junctions in natural images, and are arranged in a 3-layer And-Or graph representation. In this hierarchic model, larger graphlets are decomposed (in And-nodes) into smaller graphlets in multiple alternative ways (at Or-nodes), and parts are shared and re-used between graphlets. Then we present a compositional boosting algorithm for computing the 17 graphlets categories collectively in the Bayesian framework. The algorithm runs recursively for each node A in the And-Or graph and iterates between two steps ? bottom-up proposal and top-down validation. The bottom-up step includes two types of boosting methods. (i) Detecting instances of A (often in low resolutions) using Adaboost method through a sequence of tests (weak c...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Tianfu Wu, Gui-Song Xia, Song Chun Zhu
• Keywords: And-or Graph Representation, Common Image Structures, Compositional Boosting Algorithm, Compositional Nature, Computer Vision, CVPR 2007, Data-driven Markov Chain