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.
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
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.
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
8448018
Efficient Frequent Pattern Mining on Web Logs
Mining frequent patterns from Web logs is an important data mining task. Candidate-generation-and-test and pattern-growth are two representative frequent pattern mining approaches. We have conducted extensive experiments on real world Web log data to analyse the characteristics of Web logs and the behaviours of these two approaches on Web logs. To improve the performance of current algorithms on mining Web logs, we propose a new algorithm – Combined Frequent Pattern Mining (CFPM) to cater for Web log data specifically. We use heuristics to prune search space and reduce costs in mining so that better efficiency is achieved. Experimental results show that CFPM significantly improves the performance of the pattern-growth approach by 1.2–7.8 times on mining frequent patterns from Web logs.
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.
12946822
Partial parallel factorization in soft-decision Reed-Solomon decoding
Reed-Solomon (RS) codes have very broad applications in communications and data storage systems. The recently developed Koetter-Vardy (KV) soft-decision decoding algorithm of RS codes can achieve substantial coding gain, while has a complexity polynomial with respect to the codeword length. One of the major steps of the KV algorithm is the factorization step. A prediction-based architecture is developed in prior efforts to reduce the latency and silicon area of this step. However, the speedup can be achieved by this approach is limited by the serial nature of the factorization algorithm. The computations involved in multiple iteration levels can not be carried out simultaneously. In this work, a novel partial parallel architecture is proposed for the factorization step. The partial 2-parallel architecture can achieve a speedup of 19% over prior works.
5639888
Strategies for using additional resources in parallel hash-based join algorithms
Hash-based join is a compute- and memory-intensive algorithm. It achieves good performance and scales well to large datasets, if sufficient memory is available to hold the hash table and the distribution of computing had across nodes is balanced. We compare three adaptive algorithms that start with a partitioning of the hash table across a group of nodes and expand during the hash table building phase to additional resources, when memory on a node is used up. The split-based algorithm partitions the hash table range assigned to the node, on which memory is full, into two segments and assigns one of the segments to a new node in the system. The replication-based algorithm replicates the hash table range on a new node. The hybrid algorithm combines the first and second strategies in order to address each strategy's short comings. We perform an experimental performance evaluation of these algorithms on a PC cluster. Our results show that among the three algorithms, in most cases the hybrid algorithm either performs close to the better of the two or is the best algorithm.
41716369
Learning Object Models, Languages, Authoring Tools and Registry Service for Achieving Dynamic and Collaborative E-Learning