NDLI: A Numerical Study of the Bottom-Up and Top-Down Inference Processes in And-Or Graphs

Content Provider	Springer Nature Link
Author	Wu, Tianfu Zhu, Song Chun
Copyright Year	2010
Abstract	This paper presents a numerical study of the bottom-up and top-down inference processes in hierarchical models using the And-Or graph as an example. Three inference processes are identified for each node A in a recursively defined And-Or graph in which stochastic context sensitive image grammar is embedded: the α(A) process detects node A directly based on image features, the β(A) process computes node A by binding its child node(s) bottom-up and the γ(A) process predicts node A top-down from its parent node(s). All the three processes contribute to computing node A from images in complementary ways. The objective of our numerical study is to explore how much information each process contributes and how these processes should be integrated to improve performance. We study them in the task of object parsing using And-Or graph formulated under the Bayesian framework. Firstly, we isolate and train the α(A), β(A) and γ(A) processes separately by blocking the other two processes. Then, information contributions of each process are evaluated individually based on their discriminative power, compared with their respective human performance. Secondly, we integrate the three processes explicitly for robust inference to improve performance and propose a greedy pursuit algorithm for object parsing. In experiments, we choose two hierarchical case studies: one is junctions and rectangles in low-to-middle-level vision and the other is human faces in high-level vision. We observe that (i) the effectiveness of the α(A), β(A) and γ(A) processes depends on the scale and occlusion conditions, (ii) the α(face) process is stronger than the α processes of facial components, while β(junctions) and β(rectangle) work much better than their α processes, and (iii) the integration of the three processes improves performance in ROC comparisons.
Starting Page	226
Ending Page	252
Page Count	27
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	93
Issue Number	2
e-ISSN	15731405
Language	English
Publisher	Springer US
Publisher Date	2010-05-07
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Bottom-up/Top-down inference α–β–γ process Information contribution Hierarchical model And-Or graph Object parsing Artificial Intelligence (incl. Robotics) Image Processing and Computer Vision Pattern Recognition Computer Imaging, Vision, Pattern Recognition and Graphics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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