NDLI: Learning mixed templates for object recognition

Content Provider	IEEE Xplore Digital Library
Author	Zhangzhang Si Haifeng Gong Ying Nian Wu Song-Chun Zhu
Copyright Year	2009
Description	Author affiliation: Dept. of Stat., UCLA, Los Angeles, CA, USA (Zhangzhang Si; Haifeng Gong; Ying Nian Wu; Song-Chun Zhu)
Abstract	This article proposes a method for learning object templates composed of local sketches and local textures, and investigates the relative importance of the sketches and textures for different object categories. Local sketches and local textures in the object templates account for shapes and appearances respectively. Both local sketches and local textures are extracted from the maps of Gabor filter responses. The local sketches are captured by the local maxima of Gabor responses, where the local maximum pooling accounts for shape deformations in objects. The local textures are captured by the local averages of Gabor filter responses, where the local average pooling extracts texture information for appearances. The selection of local sketch variables and local texture variables can be accomplished by a projection pursuit type of learning process, where both types of variables can be compared and merged within a common framework. The learning process returns a generative model for image intensities from a relatively small number of training images. The recognition or classification by template matching can then be based on log-likelihood ratio scores. We apply the learning method to a variety of object and texture categories. The results show that both the sketches and textures are useful for classification, and they complement each other.
Starting Page	272
Ending Page	279
File Size	4123581
Page Count	8
File Format	PDF
ISBN	9781424439928
ISSN	10636919
DOI	10.1109/CVPR.2009.5206770
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Object recognition Histograms Statistics Shape Gabor filters Data mining Image generation Learning systems Lattices Probability density function
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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