Elastic Net Sparse Coding-based Space Object Recognition
Meng Gang · Acta Aeronautica Et Astronautica Sinica · 2013
The traditional bag-of-features(BoF) model for object recognition assumes each local feature point is related to only one visual word.Besides,sparse coding with l1-norm constraint generally selects only one feature without concern for which one is selected.A novel bag-of-features model based on elastic net sparse coding is presented in this paper.The model uses scale invariant feature transform(SIFT) feature descriptors to construct a feature dictionary,and then applies an elastic net regression model to the solution of sparse-coefficient vectors.Finally the sparse-coefficient vectors in each object image are pooled for classification.Compared with the conventional BoF model and the BoF model based on l1-norm sparse coding,our model achieves better recognition performance and is more robust to the variation of viewpoints.Experiments on the space object image database demonstrate the effectiveness of the proposed model.