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Spectral Attribute Learning for Visual Regression
Jul 24, 2017Author:
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Title: Spectral Attribute Learning for Visual Regression

 Authors: Chen, K; Jia, K; Zhang, ZX; Kamarainen, JK

 Author Full Names: Chen, Ke; Jia, Kui; Zhang, Zhaoxiang; Kamarainen, Joni-Kristian

 Source: PATTERN RECOGNITION, 66 74-81; SI 10.1016/j.patcog.2017.01.009 JUN 2017

 Language: English

 Abstract: A number of computer vision problems such as facial age estimation, crowd counting and pose estimation can be solved by learning regression mapping on low-level imagery features. We show that visual regression can be substantially improved by two-stage regression where imagery features are first mapped to an attribute space which explicitly models latent correlations across continuously-changing output. We propose an approach to automatically discover "spectral attributes" which avoids manual work required for defining hand-crafted attribute representations. Visual attribute regression outperforms direct visual regression and our spectral attribute visual regression achieves state-of-the-art accuracy in multiple applications.

 ISSN: 0031-3203

 eISSN: 1873-5142

 IDS Number: EP4TC

 Unique ID: WOS:000397371800009

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