Context augmented event and object recognition

Authors
Wang, Xiaoyang
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Other Contributors
Ji, Qiang, 1963-
Boyer, Kim L.
Franklin, W. Randolph
Mitchell, John E.
Issue Date
2015-05
Keywords
Electrical engineering
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
Fourthly, unlike traditional approaches for object recognition that treat attributes as a middle level representation and require the estimation of attributes during testing, we further propose to incorporate attributes as hidden context to improve object recognition. To achieve this goal, we develop two different approaches to incorporate attributes, with one approach utilizing attributes as additional features and the other utilizing the relationships between attributes and objects. Both approaches can effectively improve the learning of the object classifiers, and a combination of the two yields the best performance.
Description
May 2015
School of Engineering
Department
Dept. of Electrical, Computer, and Systems Engineering
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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