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 variations effectively. Experiments demonstrate the effectiveness of our method in several recognition tasks.