NDLI: Unsupervised Joint Alignment of Complex Images

Content Provider	IEEE Xplore Digital Library
Author	Huang, G.B. Jain, V. Learned-Miller, E.
Copyright Year	2007
Description	Author affiliation: Univ. of Massachusetts Amherst, Amherst (Huang, G.B.; Jain, V.; Learned-Miller, E.)
Abstract	Many recognition algorithms depend on careful positioning of an object into a canonical pose, so the position of features relative to a fixed coordinate system can be examined. Currently, this positioning is done either manually or by training a class-specialized learning algorithm with samples of the class that have been hand-labeled with parts or poses. In this paper, we describe a novel method to achieve this positioning using poorly aligned examples of a class with no additional labeling. Given a set of unaligned examplars of a class, such as faces, we automatically build an alignment mechanism, without any additional labeling of parts or poses in the data set. Using this alignment mechanism, new members of the class, such as faces resulting from a face detector, can be precisely aligned for the recognition process. Our alignment method improves performance on a face recognition task, both over unaligned images and over images aligned with a face alignment algorithm specifically developed for and trained on hand-labeled face images. We also demonstrate its use on an entirely different class of objects (cars), again without providing any information about parts or pose to the learning algorithm.
Starting Page	1
Ending Page	8
File Size	1008112
Page Count	8
File Format	PDF
ISBN	9781424416301
ISSN	15505499
DOI	10.1109/ICCV.2007.4408858
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-14
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face detection Face recognition Object detection Detectors Labeling Pipelines Image recognition Motion detection Indoor environments Layout
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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