7670540
On image matrix based feature extraction algorithms
Principal component analysis (PCA) and linear discriminant analysis (LDA) are two important feature extraction methods and have been widely applied in a variety of areas. A limitation of PCA and LDA is that when dealing with image data, the image matrices must be first transformed into vectors, which are usually of very high dimensionality. This causes expensive computational cost and sometimes the singularity problem. Recently two methods called two-dimensional PCA (2DPCA) and two-dimensional LDA (2DLDA) were proposed to overcome this disadvantage by working directly on 2-D image matrices without a vectorization procedure. The 2DPCA and 2DLDA significantly reduce the computational effort and the possibility of singularity in feature extraction. In this paper, we show that these matrices based 2-D algorithms are equivalent to special cases of image block based feature extraction, i.e., partition each image into several blocks and perform standard PCA or LDA on the aggregate of all image blocks. These results thus provide a better understanding of the 2-D feature extraction approaches.
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.
2618375
Server-Level Power Control
We present a technique that controls the peak power consumption of a high-density server by implementing a feedback controller that uses precise, system-level power measurement to periodically select the highest performance state while keeping the system within a fixed power constraint. A control theoretic methodology is applied to systematically design this control loop with analytic assurances of system stability and controller performance, despite unpredictable workloads and running environments. In a real server we are able to control power over a 1 second period to within 1 W. Additionally, we have observed that power over an 8 second period can be controlled to within 0.1 W. We believe that we are the first to demonstrate such precise control of power in a real server. Conventional servers respond to power supply constraint situations by using simple open-loop policies to set a safe performance level in order to limit peak power consumption. We show that closed-loop control can provide higher performance under these conditions and test this technique on an IBM BladeCenter HS20 server. Experimental results demonstrate that closed-loop control provides up to 82% higher application performance compared to open-loop control and up to 17% higher performance compared to a widely used ad-hoc technique.
15818943
Tracking anonymous peer-to-peer VoIP calls on the internet
Peer-to-peer VoIP calls are becoming increasingly popular due to their advantages in cost and convenience. When these calls are encrypted from end to end and anonymized by low latency anonymizing network, they are considered by many people to be both secure and anonymous.In this paper, we present a watermark technique that could be used for effectively identifying and correlating encrypted, peer-to-peer VoIP calls even if they are anonymized by low latency anonymizing networks. This result is in contrast to many people's perception. The key idea is to embed a unique watermark into the encrypted VoIP flow by slightly adjusting the timing of selected packets. Our analysis shows that it only takes several milliseconds time adjustment to make normal VoIP flows highly unique and the embedded watermark could be preserved across the low latency anonymizing network if appropriate redundancy is applied. Our analytical results are backed up by the real-time experiments performed on leading peer-to-peer VoIP client and on a commercially deployed anonymizing network. Our results demonstrate that (1) tracking anonymous peer-to-peer VoIP calls on the Internet is feasible and (2) low latency anonymizing networks are susceptible to timing attacks.
3114897
Multi-graph enabled active learning for multimodal web image retrieval
In this paper, we propose a multimodal Web image retrieval technique based on multi-graph enabled active learning. The main goal is to leverage the heterogeneous data on the Web to improve retrieval precision. Three graphs are constructed on images' content features, textual annotations and hyperlinks respectively, namely Content-Graph, Text-Graph and Link-Graph, which provide complimentary information on the images. By analyzing the three graphs, a training dataset is automatically created and transductive learning is enabled. The transductive learner is a multi-graph based classifier, which simultaneously solves the learning problem and the problem of combining heterogeneous data. This proposed approach, overall, tackles the problem of unsupervised active learning on Web graph. Although the proposed approach is discussed in the context of WWW image retrieval, it can be applied to other domains. The experimental results show the effectiveness of our approach.
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.
16978288
Interactive modeling of tree bark
There exist many computer graphics techniques which could achieve high quality tree generation. However, only few works focus on realistic modeling of tree bark. Difficulties lie in the complex appearance of the bark surfaces from a single image. We address three main issues here: feature specification; height field assignment; and texture correction. For feature specification, we use texton channel analysis to specify a variant of common bark features, inluding ironbark, vertical and horizontal fractures, tessellation, furrowed cork, and lenticels. For height field assignment, we develop an intuitive and easy-to-use user interface (UI). Here similarity-based texture editing is used for assigning height fields within a texton channel mask. For texture correction, we use the modeled height fields to eliminate the underlying lighting effects in a captured texture. Our modeling system is image-based: it takes as input a bark image and produces as output a textured height field representing a bark sample. We demonstrate that out method is an effective and easy-to-use technique to interactively model a variety of photo realistic bark surfaces.