An Analysis and Application of Fast Nonnegative Orthogonal Matching Pursuit for Image Categorization in Deep Networks

Nonnegative orthogonal matching pursuit (NOMP) has been proven to be a more stable encoder for unsupervised sparse representation learning. However, previous research has shown that NOMP is suboptimal in terms of computational cost, as the coefficients selection and refinement using nonnegative least squares (NNLS) have been divided into two separate steps. It is found that this problem severely reduces the efficiency of encoding for large-scale image patches. In this work, we study fast nonnegative OMP (FNOMP) as an efficient encoder which can be accelerated by the implementation of factorization and iterations of coefficients in deep networks for full-size image categorization task. It is analyzed and demonstrated that using relatively simple gain-shape vector quantization for training dictionary, FNOMP not only performs more efficiently than NOMP for encoding but also significantly improves the classification accuracy compared to OMP based algorithm. In addition, FNOMP based algorithm is superior to other state-of-the-art methods on several publicly available benchmarks, that is, Oxford Flowers, UIUC-Sports, and Caltech101.