NDLI: Discriminatively Trained And-Or Tree Models for Object Detection

Content Provider	IEEE Xplore Digital Library
Author	Xi Song Tianfu Wu Yunde Jia Song-Chun Zhu
Copyright Year	2013
Description	Author affiliation: Dept. of Stat., Univ. of California, Los Angeles, Los Angeles, CA, USA (Tianfu Wu; Song-Chun Zhu) \|\| Lab. of Intell. Inf. Technol., Beijing Inst. of Technol., Beijing, China (Xi Song; Yunde Jia)
Abstract	This paper presents a method of learning reconfigurable And-Or Tree (AOT) models discriminatively from weakly annotated data for object detection. To explore the appearance and geometry space of latent structures effectively, we first quantize the image lattice using an over complete set of shape primitives, and then organize them into a directed a cyclic And-Or Graph (AOG) by exploiting their compositional relations. We allow overlaps between child nodes when combining them into a parent node, which is equivalent to introducing an appearance Or-node implicitly for the overlapped portion. The learning of an AOT model consists of three components: (i) Unsupervised sub-category learning (i.e., branches of an object Or-node) with the latent structures in AOG being integrated out. (ii) Weakly supervised part configuration learning (i.e., seeking the globally optimal parse trees in AOG for each sub-category). To search the globally optimal parse tree in AOG efficiently, we propose a dynamic programming (DP) algorithm. (iii) Joint appearance and structural parameters training under latent structural SVM framework. In experiments, our method is tested on PASCAL VOC 2007 and 2010 detection benchmarks of 20 object classes and outperforms comparable state-of-the-art methods.
Starting Page	3278
Ending Page	3285
File Size	6963753
Page Count	8
File Format	PDF
ISBN	9780769549897
ISSN	10636919
DOI	10.1109/CVPR.2013.421
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Object detection Space exploration Lattices Shape Training Support vector machines Deformable models Latent Structural SVM And-Or Graph Part-based Representation Object Detection
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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