13915062
Intrapersonal subspace analysis with application to adaptive Bayesian face recognition
We propose a subspace distance measure to analyze the similarity between intrapersonal face subspaces, which characterize the variations between face images of the same individual. We call the conventional intrapersonal subspace the average intrapersonal subspace (AIS) because the image differences often come from a large number of persons. We call an intrapersonal subspace specific intrapersonal subspace (SIS) if the image differences are from just one person. We demonstrate that SIS varies from person to person and most SISs are not similar to AIS. Based on these observations, we introduce the maximum a posteriori (MAP) adaptation to the problem of SIS estimation, and apply it to the Bayesian face recognition algorithm. Experimental results show that the adaptive Bayesian algorithm outperforms the non-adaptive Bayesian algorithm as well as Eigenface and Fisherface methods when a small number of adaptation images are available.
8081051
The equivalence of two-dimensional PCA to line-based PCA
The state-of-the-art in human face recognition is the subspace methods originated by the Principal Component Analysis (PCA), the Eigenfaces of the facial images. Recently, a technique called Two-dimensional PCA (2DPCA) was proposed for human face representation and recognition. It was developed for image feature extraction based on 2D matrices as opposed to the standard PCA, which is based on 1D vectors. In this note, we show that 2DPCA is equivalent to a special case of an existing feature extraction method, block-based PCA, which has been used for face recognition in a number of systems.
28581906
Design of an OGSA-Based MetaService Architecture
This paper proposes a MetaService-based architecture for dealing with the problem of services management in grid systems. We introduce the basic idea of MetaService, and then discuss MetaService classification, new MetaService construction, and the MetaService working mechanism. A graph and a tree can be used for the optimization function of MetaService. An example from real life is used to explain the role of MetaService. We give an overview of a grid prototype system which has incorporated some of the ideas presented. A brief comparison is given between MetaService and other related work.
36117076
Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences
An important usage of time sequences is to discover temporal patterns. The discovery process usually starts with a user specified skeleton, called an event structure, which consists of a number of variables representing events and temporal constraints among these variables; the goal of the discovery is to find temporal patterns, i.e., instantiations of the variables in the structure that appear frequently in the time sequence. The paper introduces event structures that have temporal constraints with multiple granularities, defines the pattern discovery problem with these structures, and studies effective algorithms to solve it. The basic components of the algorithms include timed automata with granularities (TAGs) and a number of heuristics. The TAGs are for testing whether a specific temporal pattern, called a candidate complex event type, appears frequently in a time sequence. Since there are often a huge number of candidate event types for a usual event structure, heuristics are presented aiming at reducing the number of candidate event types and reducing the time spent by the TAGs testing whether a candidate type does appear frequently in the sequence. These heuristics exploit the information provided by explicit and implicit temporal constraints with granularity in the given event structure. The paper also gives the results of an experiment to show the effectiveness of the heuristics on a real data set.
15083897
Grouping web image search result
In this paper, we propose a Web image search result organizing method to facilitate user browsing. We formalize this problem as a salient image region pattern extraction problem. Given the images returned by Web search engine, we first segment the images into homogeneous regions and quantize the environmental regions into image codewords. The salient codeword "phrases" are then extracted and ranked based on a regression model learned from human labeled training data. According to the salient "phrases", images are assigned to different clusters, with the one nearest to the centroid as the entry for the corresponding cluster. Satisfying experimental results show the effectiveness of our proposed method.
810413
Enhancing the robustness of distributed real-time middleware via end-to-end utilization control
A key challenge for distributed real-time and embedded (DRE) middleware is maintaining both system reliability and desired real-time performance in unpredictable environments where system workload and resources may fluctuate significantly. This paper presents FC-ORB, a realtime object request broker (ORB) middleware that employs end-to-end utilization control to handle fluctuations in application workload and system resources. The contributions of this paper are three-fold. First, we present a novel utilization control service that enforces desired CPU utilization bounds on multiple processors by adapting the rates of end-to-end tasks within user-specified ranges. Second, we describe a set of middleware-level mechanisms designed to support end-to-end tasks and distributed multi-processor utilization control in a real-time ORB. Finally, we present extensive experimental results on a Linux testbed. Our results demonstrate that our middleware can maintain desired utilizations in face of uncertainties and variations in task execution times, resource contentions from external workloads, and permanent processor failure. FC-ORB demonstrates that the integration of utilization control, end-to-end scheduling and fault-tolerance mechanisms in DRE middleware is a promising approach for enhancing the robustness of DRE applications in unpredictable environments
14477369
Markov random field modeled range image segmentation
In this paper, range image segmentation is studied in the framework of the maximum a posteriori estimation and Markov random field modeling. A novel range image segmentation model is proposed. The model serves as an evaluator for a small number of segmentation candidates obtained through a fast edge detection algorithm. A local method is employed to search for the optimal segmentation from the candidates. Experimental results show that such combination of heuristics and model-based evaluation leads to a fast and accurate segmentation.