16804646
Mean quantization blind watermarking for image authentication
The objective of this paper is to propose an image authentication scheme, which is able to detect malicious tampering of images even they have also been incidentally distorted. By modeling incidental and malicious distortions as Gaussian distributions with small and large variances, respectively, we propose to embed a watermark in the wavelet domain by a mean quantization technique. Due to the various probabilities of tamper response at each scale, these responses are integrated to make a decision on the tampered areas. Statistical analysis is conducted and experimental results are given to demonstrate that our watermarking scheme is able to detect malicious attacks while tolerating incidental distortions.
10243856
A new LDA-based face recognition system which can solve the small sample size problem
A new LDA-based face recognition system is presented in this paper. Linear discriminant analysis (LDA) is one of the most popular linear projection techniques for feature extraction. The major drawback of applying LDA is that it may encounter the small sample size problem. In this paper, we propose a new LDA-based technique which can solve the small sample size problem. We also prove that the most expressive vectors derived in the null space of the within-class scatter matrix using principal component analysis (PCA) are equal to the optimal discriminant vectors derived in the original space using LDA. The experimental results show that the new LDA process improves the performance of a face recognition system signi"cantly. ( 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
10985120
Approximation Algorithms for the k-Clique Covering Problem
The problem of covering edges and vertices in a graph (or in a hypergraph) was motivated by a problem arising in the context of the component assembly problem. The problem is as follows: given a graph and a clique size $k$, find the minimum number of $k$-cliques such that all edges and vertices of the graph are covered by (included in) the cliques. This paper provides a collection of approximation algorithms for various clique sizes with proven worst-case bounds. The problem has a natural extension to hypergraphs, for which we consider one particular class. The $k$-clique covering problem can be formulated as a set covering problem. It is shown that the algorithms we design, which exploit the structure of this special set covering problem, have better performance than those derived from direct applications of general purpose algorithms for the set covering. In particular, these special classes of set covering problems can be solved with better worst-case bounds and/or complexity than if treated as general set covering problems.