کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
527228 | 869304 | 2009 | 7 صفحه PDF | دانلود رایگان |
Subspace manifold learning represents a popular class of techniques in statistical image analysis and object recognition. Recent research in the field has focused on nonlinear representations; locally linear embedding (LLE) is one such technique that has recently gained popularity. We present and apply a generalization of LLE that introduces sample weights. We demonstrate the application of the technique to face recognition, where a model exists to describe each face’s probability of occurrence. These probabilities are used as weights in the learning of the low-dimensional face manifold. Results of face recognition using this approach are compared against standard nonweighted LLE and PCA. A significant improvement in recognition rates is realized using weighted LLE on a data set where face occurrences follow the modeled distribution.
Journal: Image and Vision Computing - Volume 27, Issues 1–2, 1 January 2009, Pages 80–86