6625111
On incremental and robust subspace learning
Principal Component Analysis (PCA) has been of great interest in computer vision and pattern recognition. In particular, incrementally learning a PCA model, which is computationally efficient for large-scale problems as well as adaptable to reflect the variable state of a dynamic system, is an attractive research topic with numerous applications such as adaptive background modelling and active object recognition. In addition, the conventional PCA, in the sense of least mean squared error minimisation, is susceptible to outlying measurements. To address these two important issues, we present a novel algorithm of incremental PCA, and then extend it to robust PCA. Compared with the previous studies on robust PCA, our algorithm is computationally more efficient. We demonstrate the performance of these algorithms with experimental results on dynamic background modelling and multi-view face modelling.
1164650
Constructing Facial Identity Surfaces for Recognition
We present a novel approach to face recognition by constructing facial identity structures across views and over time, referred to as identity surfaces, in a Kernel Discriminant Analysis (KDA) feature space. This approach is aimed at addressing three challenging problems in face recognition: modelling faces across multiple views, extracting non-linear discriminatory features, and recognising faces over time. First, a multi-view face model is designed which can be automatically fitted to face images and sequences to extract the normalised facial texture patterns. This model is capable of dealing with faces with large pose variation. Second, KDA is developed to compute the most significant non-linear basis vectors with the intention of maximising the between-class variance and minimising the within-class variance. We applied KDA to the problem of multi-view face recognition, and a significant improvement has been achieved in reliability and accuracy. Third, identity surfaces are constructed in a pose-parameterised discriminatory feature space. Dynamic face recognition is then performed by matching the object trajectory computed from a video input and model trajectories constructed on the identity surfaces. These two types of trajectories encode the spatio-temporal dynamics of moving faces.
5262698
An integrated algorithm of incremental and robust PCA
Principal component analysis (PCA) is a well-established technique in image processing and pattern recognition. Incremental PCA and robust PCA are two interesting problems with numerous potential applications. However, these two issues have only been separately addressed in the previous studies. In this paper, we present a novel algorithm for incremental and robust PCA by seamlessly integrating the two issues together. The proposed algorithm has the advantages of both incremental PCA and robust PCA. Moreover, unlike most M-estimation based robust algorithms, it is computational efficient. Experimental results on dynamic background modelling are provided to show the performance of the algorithm with a comparison to the conventional batch-mode and nonrobust algorithms.
737421
Recognising trajectories of facial identities using kernel discriminant analysis
We present a comprehensive approach to address three challenging problems in face recognition: modelling faces across multi-views, extracting the nonlinear discriminating features, and recognising moving faces dynamically in image sequences. A multi-view dynamic face model is designed to extract the shape-and-pose-free facial texture patterns. Kernel discriminant analysis, which employs the kernel technique to perform linear discriminant analysis in a high-dimensional feature space, is developed to extract the significant nonlinear features which maximise the between-class variance and minimise the within-class variance. Finally, an identity surface based face recognition is performed dynamically from video input by matching object and model trajectories. q 2003 Elsevier B.V. All rights reserved.
5886737
Using KCCA for Japanese–English cross-language information retrieval and document classification
Kernel Canonical Correlation Analysis (KCCA) is a method of correlating linear relationship between two variables in a kernel defined feature space. A machine learning algorithm based on KCCA is studied for cross-language information retrieval. We apply the algorithm in Japanese–English cross-language information retrieval. The results are quite encouraging and are significantly better than those obtained by other state of the art methods. Computational complexity is an important issue when applying KCCA to large dataset as in information retrieval. We experimentally evaluate several methods to alleviate the problem of applying KCCA to large datasets. We also investigate cross-language document classification using KCCA as well as other methods. Our results show that it is feasible to use a classifier learned in one language to classify the documents in other languages.
1111436
Adaptive resource management in active network nodes
Resource management in active network nodes is challenging because more types and amount of resources are needed compared with traditional networks, and the consumption of different types of resources is not equally balanced. Hence, it is possible and important to consider the relationship between different types of resources, needed by an application. Adaptation of applications resource usages in network nodes enables the applications to react promptly to the resource status in the network nodes and therefore offers a better performance guarantee and more flexibility to the applications. This paper studies the resource management in active network nodes considering the relationship among different types of resources. The resource vectors (RVs) are introduced to describe the resource requirements of applications and their adaptation capabilities. And an adaptive resource management mechanism based on RV is proposed and the implementation of it in an active node is given.
45088529
Chaining watermarks for detecting malicious modifications to streaming data
Abstract Authenticating streaming data is a very important research area due to its wide range of applications. Previous technologies mainly focused on authenticating data packets at the IP layer and ensuring the robustness of the verification. These schemes usually incur large communications overhead, which is not desirable in applications with limited bandwidth. In this paper, we propose a novel fragile watermarking algorithm which verifies the integrity of streaming data at the application layer. The data are divided into groups based on synchronization points, so each group can be synchronized and any modifications made to one group only affect up to two groups. A unique watermark is embedded directly into each group to save communications bandwidth. The embedded watermark can detect as well as locate any modifications made to a data stream. To ensure the completeness of the data stream, watermarks are chained across groups so that no matter how many data are deleted, these deletions can be correctly detected. Security analysis and experimental results show that the proposed scheme can efficiently detect and locate modifications and ensure the completeness of data streams.
8083367
On Some Three-Color Ramsey Numbers
Abstract. In this paper we study three-color Ramsey numbers. Let Ki,j denote a complete i by j bipartite graph. We shall show that (i) for any connected graphs G1, G2 and G3, if r(G1, G2)≥s(G3), then r(G1, G2, G3)≥(r(G1, G2)−1)(χ(G3)−1)+s(G3), where s(G3) is the chromatic surplus of G3; (ii) (k+m−2)(n−1)+1≤r(K1,k, K1,m, Kn)≤ (k+m−1)(n−1)+1, and if k or m is odd, the second inequality becomes an equality; (iii) for any fixed m≥k≥2, there is a constant c such that r(Kk,m, Kk,m, Kn)≤c(n/logn), and r(C2m, C2m, Kn)≤c(n/logn)m/(m−1) for sufficiently large n.
14562465
Bioinformatics on Embedded Systems: A Case Study of Computational Biology Applications on VLIW Architecture
Bioinformatics applications represent the increasingly important workloads. Their characteristics and implications on the underlying hardware design, however, are largely unknown. Currently, biological data processing ubiquitously relies on the high-end systems equipped with expensive, general-purpose processors. The future generation of bioinformatics requires the more flexible and cost-effective computing platforms to meet its rapidly growing market. The programmable, application-specific embedded systems appear to be an attractive solution in terms of easy of programming, design cost, power, portability and time-to-market. The first step towards such systems is to characterize bioinformatics applications on the target architecture. Such studies can help in understanding the design issues and the trade-offs in specializing hardware and software systems to meet the needs of bioinformatics market. This paper evaluates several representative bioinformatics tools on the VLIW based embedded systems. We investigate the basic characteristics of the benchmarks, impact of function units, the efficiency of VLIW execution, cache behavior and the impact of compiler optimizations. The architectural implications observed from this study can be applied to the design optimizations. To the best of our knowledge, this is one of the first such studies that have ever been attempted.