5695951
Video Event Detection using ICA Mixture Hidden Markov Models
In this paper, a framework that combines feature extraction, model learning, and likelihood computation, is presented for video event detection. First, the independent component analysis (ICA) is applied to the raw feature space to extract the spatial features. Then, a framework based on ICA mixture hidden Markov models (ICAMHMM) is used to exploit the spatial and temporal characteristics of the training data. After the model is learnt, the likelihood for a given video sequence is computed and then used to classify the video into a semantic event. Golf video sequences are used for simulations. The results show that the proposed method can effectively detect semantic video events.
1661896
Multiscale Fragile Watermarking Based on the Gaussian Mixture Model
In this paper, a new multiscale fragile watermarking scheme based on the Gaussian mixture model (GMM) is presented. First, a GMM is developed to describe the statistical characteristics of images in the wavelet domain and an expectation-maximization algorithm is employed to identify GMM model parameters. With wavelet multiscale subspaces being divided into watermarking blocks, the GMM model parameters of different watermarking blocks are adjusted to form certain relationships, which are employed for the presented new fragile watermarking scheme for authentication. An optimal watermark embedding method is developed to achieve minimum watermarking distortion. A secret embedding key is designed to securely embed the fragile watermarks so that the new method is robust to counterfeiting, even when the malicious attackers are fully aware of the watermark embedding algorithm. It is shown that the presented new method can securely embed a message bit stream, such as personal signatures or copyright logos, into a host image as fragile watermarks. Compared with conventional fragile watermark techniques, this new statistical model based method modifies only a small amount of image data such that the distortion on the host image is imperceptible. Meanwhile, with the embedded message bits spreading over the entire image area through the statistical model, the new method can detect and localize image tampering. Besides, the new multiscale implementation of fragile watermarks based on the presented method can help distinguish some normal image operations such as JPEG compression from malicious image attacks and, thus, can be used for semi-fragile watermarking
5541899
Automatic identification of digital video based on shot-level sequence matching
To locate a video clip in large collections is very important for retrieval applications, especially for digital rights management. In this paper, we present a novel technique for automatic identification of digital video. This new algorithm is based on dynamic programming that fully uses the temporal dimension to measure the similarity between two video sequences. A normalized chromaticity histogram is used as a feature which is illumination-invariant. Dynamic programming is applied on shot-level to find the optimal nonlinear mapping between video sequences. Two new normalized distance measures are presented for video sequence matching. One measure is based on the normalization of the optimal path found by dynamic programming. The other measure combines both the visual features and the temporal information. Experimental results show that the shot-level approach is robust to frame rate conversion, color correction, and compressions. The proposed distance measures are suitable for variable-length comparisons.
18569940
Segmentation of bright targets using wavelets and adaptive thresholding
A general systematic method for the detection and segmentation of bright targets is developed. We use the term "bright target" to mean a connected, cohesive object which has an average intensity distribution above that of the rest of the image. We develop an analytic model for the segmentation of targets, which uses a novel multiresolution analysis in concert with a Bayes classifier to identify the possible target areas. A method is developed which adaptively chooses thresholds to segment targets from background, by using a multiscale analysis of the image probability density function (PDF). A performance analysis based on a Gaussian distribution model is used to show that the obtained adaptive threshold is often close to the Bayes threshold. The method has proven robust even when the image distribution is unknown. Examples are presented to demonstrate the efficiency of the technique on a variety of targets.
13183312
Wavelet based automatic thresholding for image segmentation
In this paper, a new systematic method to segment possible target areas based on wavelet transforms is presented. We develop an analytic model for the segmentation of targets, which uses a novel multiresolution analysis in concert with a Bayesian classifier to identify the possible target areas. A method is developed which adaptively chooses thresholds to segment targets from background, by using a multiscale analysis of the image probability density function (PDF). We present examples which demonstrate the efficiency of the technique on a variety of targets.
8152868
A web-enabled video indexing system
Video parsing and indexing is an important early stage of content-based video analysis. In this paper, we present a new web-enabled video indexing system that integrates Synchronized Multimedia Integration Language (SMIL) standard. New algorithms are proposed for video temporal segmentation. Sharp transition detection is achieved by an enhanced histogram-based method that is robust to illumination changes. For gradual transition detection, new features are introduced for dissolve detection. The proposed dissolve detector is based on a combined analysis of mean-variance-skewness of intensity. Compared with existing variance-based approaches, the introduced new features improve the discrimination ability on shot boundaries. We also describe methods for eliminating false positives. Experimental results show that the proposed algorithms can effectively detect shot boundaries. Detected scenes and other cinematic attributes are structured and organized by integrating HTML and SMIL. For each video file, the system generates a table-of-contents indexing file. The user-friendly interface provides web-based interaction, browsing and previewing of video content
17630030
Prediction of Protein Complexes Based on Protein Interaction Data and Functional Annotation Data Using Kernel Methods
Prediction of protein complexes is a crucial problem in computational biology. The increasing amount of available genomic data can enhance the identification of protein complexes. Here we describe an approach for predicting protein complexes based on integration of protein-protein interaction (PPI) data and protein functional annotation data. The basic idea is that proteins in protein complexes often interact with each other and protein complexes exhibit high functional consistency/even multiple functional consistency. We create a protein-protein relationship network (PPRN) via a kernel-based integration of these two genomic data. Then we apply the MCODE algorithm on PPRN to detect network clusters as numerically determined protein complexes. We present the results of the approach to yeast Sacchromyces cerevisiae. Comparison with well-known experimentally derived complexes and results of other methods verifies the effectiveness of our approach.
18753637
Fast factorization architecture in soft-decision Reed-Solomon decoding
Reed-Solomon (RS) codes are among the most widely utilized block error-correcting codes in modern communication and computer systems. Compared to its hard-decision counterpart, soft-decision decoding offers considerably higher error-correcting capability. The recent development of soft-decision RS decoding algorithms makes their hardware implementations feasible. Among these algorithms, the Koetter-Vardy (KV) algorithm can achieve substantial coding gain for high-rate RS codes, while maintaining a polynomial complexity with respect to the code length. In the KV algorithm, the factorization step can consume a major part of the decoding latency. A novel architecture based on root-order prediction is proposed in this paper to speed up the factorization step. As a result, the time-consuming exhaustive-search-based root computation in each iteration level, except the first one, of the factorization step is circumvented with more than 99% probability. Using the proposed architecture, a speedup of 141% can be achieved over prior efforts for a (255, 239) RS code, while the area consumption is reduced to 31.4%.
17218997
Profiling program behavior for anomaly intrusion detection based on the transition and frequency property of computer audit data
Intrusion detection is an important technique in the defense-in-depth network security framework. In recent years, it has been a widely studied topic in computer network security. In this paper, we present two methods, namely, the Hidden Markov Models (HMM) method and the Self Organizing Maps (SOM) method, to profile normal program behavior for anomaly intrusion detection based on computer audit data. The HMM method utilizes the transition property of events while SOM method relies on the frequency property of events. Two data sets, CERT synthetic Sendmail system call data collected in the University of New Mexico (UNM) and Live FTP system call data collected in the CNSIS lab of Xi'an Jiaotong University, were used to assess the two methods. Testing results show that the HMM method using the transition property of events produces good detection performance while high computational expense is required both for training and detection. The HMM method is better than other two methods reported previously in terms of detection accuracy for the same data set. The SOM method considering the frequency property of events, on the other hand, is suitable for real-time intrusion detection because of its capability of processing a large amount of data with low computational overhead.
14773046
Predicting methylation status of CpG islands in the human brain
MOTIVATION
Over 50% of human genes contain CpG islands in their 5'-regions. Methylation patterns of CpG islands are involved in tissue-specific gene expression and regulation. Mis-epigenetic silencing associated with aberrant CpG island methylation is one mechanism leading to the loss of tumor suppressor functions in cancer cells. Large-scale experimental detection of DNA methylation is still both labor-intensive and time-consuming. Therefore, it is necessary to develop in silico approaches for predicting methylation status of CpG islands.


RESULTS
Based on a recent genome-scale dataset of DNA methylation in human brain tissues, we developed a classifier called MethCGI for predicting methylation status of CpG islands using a support vector machine (SVM). Nucleotide sequence contents as well as transcription factor binding sites (TFBSs) are used as features for the classification. The method achieves specificity of 84.65% and sensitivity of 84.32% on the brain data, and can also correctly predict about two-third of the data from other tissues reported in the MethDB database.


AVAILABILITY
An online predictor based on MethCGI is available at http://166.111.201.7/MethCGI.html


CONTACT
mzhang@cshl.edu


SUPPLEMENTARY INFORMATION
Supplementary data available at Bioinformatics online and http://166.111.201.7/help.html.
17933107
Safety analysis of usage control authorization models
The usage control (UCON) model was introduced as a unified approach to capture a number of extensions for traditional access control models. While the policy specification flexibility and expressive power of this model have been studied in previous work, as a related and fundamental problem, the safety analysis of UCON has not been explored. This paper presents two fundamental safety results for UCONA, a sub-model of UCON only considering authorizations. In UCONA, an access control decision is based on the subject and/or the object attributes, which can be changed as the side-effects of using the access right, resulting in possible changes to future access control decisions. Hence the safety question in UCONA is all the more pressing since every access can potentially enable additional permissions due to the mutability of attributes in UCON. In this paper, first we show that the safety problem is in general undecidable. Then, we show that a restricted form of UCONA with finite attribute value domains and acyclic attribute creation relation has a decidable safety property. The decidable model maintains good expressive power as shown by specifying an RBAC system with a specific user-role assignment scheme and a DRM application with consumable rights.