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Discriminant Additive Tangent Spaces for Object Recognition

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Discriminant Additive Tangent Spaces for Object Recognition
Pattern variation is a major factor that affects the performance of recognition systems. In this paper, a novel manifold tangent modeling method called Discriminant Additive Tangent Spaces (DATS) is proposed for invariant pattern recognition. In DATS, intra-class variations for traditional tangent learning are called positive tangent samples. In addition, extra-class variations are introduced as negative tangent samples. We use log-odds to measure the significance of samples being positive or negative, and then directly characterizes this log-odds using generalized additive models (GAM). This model is estimated to maximally discriminate positive and negative samples. Besides, since traditional GAM fitting algorithm can not handle the high dimensional data in visual recognition tasks, we also present an efficient, sparse solution for GAM estimation. The resulting DATS is a nonparametric discriminant model based on quite weak prior hypotheses, hence it can depict various pattern variati...
Liang Xiong, Jianguo Li, Changshui Zhang
Added 12 Oct 2009
Updated 28 Oct 2009
Type Conference
Year 2007
Where CVPR
Authors Liang Xiong, Jianguo Li, Changshui Zhang
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