Our paper presents a novel high dimensional probability density estimation technique using any dimensionality reduction method. Our method first performs subspace reduction using any matrix factorization algorithm and estimates the density in the low-dimensional space using sample-point variable bandwidth kernel density estimation. Subsequently, the high dimensional density is approximated from the low dimensional density parameters. The reconstruction error due to dimensionality reduction process is also modeled in a principled and efficient manner to obtain the high dimensional density estimate. We show the effectiveness of our technique by using two popular dimensionality reduction tools, principal component analysis and non-negative matrix factorization. This technique is applied to AT&T, Yale, Pointing'04 and CMU-PIE face recognition datasets and improved performance compared to other dimensionality reduction and density estimation algorithms is obtained.

In this paper, we present a novel video fingerprinting algorithm which leverages the concept of perceptual similarity between different video sequences. Inspired by the popular structural similarity (SSIM) index, we quantify the perceptual similarity between different video sequences by proposing a perceptual distance metric (PDM) which is utilized in the matching stage of our proposed video fingerprinting algorithm. PDM requires very simple features, viz., block means and therefore has extremely low complexity in both the feature extraction part, as well as during the matching stage. We also show how to use an order statistic in the proposed distance measure to improve the system performance for localized block-based artifacts such as the logo artifact. Simulation results for the proposed fingerprinting algorithm show significant gains over other video fingerprinting techniques on different video datasets for numerous heavy video artifacts.