The heavy-tailed distributions of corrupted outliers and singular values of all channels in low-
level vision have proven effective priors for many applications such as background …

Classification is one of the most frequent tasks in machine learning. However, the variety of
classification tasks as well as classifier methods is huge. Thus the question is coming up …

We propose two nuclear-and L2, 1-norm regularized 2D neighborhood preserving
projection (2DNPP) methods for extracting representative 2D image features. 2DNPP …

In this paper, we address the semisupervised distance metric learning problem and its
applications in classification and image retrieval. First, we formulate a semisupervised …

The 2-D principal component analysis (2-DPCA) is a widely used method for image feature
extraction. However, it can be equivalently implemented via image-row-based principal …

Principal component analysis (PCA) is a widely used tool for dimensionality reduction and
feature extraction in the field of computer vision. Traditional PCA is sensitive to outliers …

Learning vector quantization applying non-standard metrics became quite popular for
classification performance improvement compared to standard approaches using the …

Dealing with partial occlusion or illumination is one of the most challenging problems in
image representation and classification. In this problem, the characterization of the …

Occlusion, real disguise and illumination are still the common difficulties encountered in face
recognition. The sparse representation based classifier (SRC) has shown a great potential …

Metric learning has attracted significant attention in the past decades, because of its
appealing advances in various real-world tasks, eg, person re-identification and face …