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
44391380
Bi-directional gradient labeling and registration for gray-scale image segmentation
Watershed is one of the commonly used methods for image segmentation. In this paper, we introduce a new segmentation scheme based on bi-directional labeling and registration and prove that its segmentation performance is equivalent to that of conventional watershed but it is much more computational efficient The bi-directional labeling and registration scheme, which will be referred to as BIDS, involves only linear scans of image pixels. It uses one dimensional operations instead of queues while traditional segmentation algorithms are two dimensional problems. BIDS also provides unique labels for each homogeneous regions. In addition to achieving the same segmentation results as conventional watershed, BIDS is four times less computationally complex than the conventional watersheds by immersion.
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.
1464666
Feedforward neural networks with multilevel hidden neurons for remotely sensed image classification
Artificial neural network has been, used as a powerful tool for pattern classification. However, it is difficult to train when the data exhibit non-sparse or overlapping pattern classes which is often the case in practical applications. In this paper, we introduce the feedforward neural network with the hidden layer consisting of multilevel neurons. The convergence property of one-layer neural network with multilevel neurons is proved. The new feedforward model is inherently capable of fuzzy pattern classification of non-sparse or overlapping pattern classes. As an application, we apply the network for the classification of LANDSAT TM data. The results show that this approach produces better results compared with conventional neural networks.
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
14579845
Texture image retrieval based on a Gaussian mixture model and similarity measure using a Kullback divergence
In a content-based image retrieval (CBIR) system, indexing feature vectors and the similarity measure between feature vectors are two key factors for retrieval performance. We present a new CBIR system with statistical-model based image feature extraction in the wavelet domain and a Kullback divergence based similarity measure. A two component Gaussian mixture model (GMM) in the wavelet domain is employed and the model parameters are used to form features for image indexing. A new Kullback divergence based similarity measure is then presented for image retrieval. The experimental results demonstrate that the similarity measure based on the Kullback divergence is more effective than conventional similarity measures, such as the city-block distance and the Euclidean distance. It is shown that the new CBIR system, with the combination of the GMM and the new Kullback divergence based similarity measure, outperforms most other methods in retrieval performance for texture images, while keeping a comparable level of computational complexity.
10484270
Obligatory activator-polymerase addition order at promoters
The kinetics of open complex formation were measured by migration retardation assay and DNase I footprinting at the activator-dependent promoters ara P1, lac P1 and gal P1. In each case, the rate of open complex formation was significantly faster if the activator, AraC for ara and CAP for lac and gal, had been added before RNA polymerase. The results indicate that complexes of transcriptional activators, RNA polymerase and promoter can exist in two states, one which can form open complexes rapidly and one which cannot.
16051355
DPPC-RE: TCAM-based distributed parallel packet classification with range encoding
Packet classification has been a critical data path function for many emerging networking applications. An interesting approach is the use of ternary content addressable memory (TCAM) to achieve deterministic, high-speed packet classification performance. However, apart from high cost and power consumption, due to slow growing clock rate for memory technology, in general, the traditional single TCAM-based solution has difficulty to keep up with fast growing line rates. Moreover, the TCAM storage efficiency is largely affected by the need to support rules with ranges or range matching. In this paper, a distributed TCAM scheme that exploits chip-level-parallelism is proposed to greatly improve the throughput performance. This scheme seamlessly integrates with a range encoding scheme which not only solves the range matching problem, but also ensures a balanced high throughput performance. A thorough theoretical worst-case analysis of throughput, processing delay, and power consumption, as well as the experimental results show that the proposed solution can achieve scalable throughput performance matching up to OC768 line rate or higher. The added TCAM storage overhead is found to be reasonably small for the five real-world classifiers studied
11467593
Generating Multidimensional Schemata from Relational Aggregation Queries
Queries on operational databases can be viewed as a form of business rules on data warehouse schema design. We propose to use such queries to automatically generate measures, dimensions and dimension hierarchies and their representation in a star schema. The schema produced with our approach has good properties such as non-redundant dimensional attributes and orthogonality among dimensions and can answer many more queries than just those it was generated from.
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.
62769950
Assessment of the effects of user characteristics on mental models of information retrieval systems
This article reports the results of a study that investigated effects of four user characteristics on users' mental models of information retrieval systems: educational and professional status, first language, academic background, and computer experience. The repertory grid technique was used in the study. Using this method, important components of information retrieval systems were represented by nine concepts, based on four IR experts' judgments. Users' mental models were represented by factor scores that were derived from users' matrices of concept ratings on different attributes of the concepts. The study found that educational and professional status, academic background, and computer experience had significant effects in differentiating users on their factor scores. First language had a borderline effect, but the effect was not significant enough at α = 0.05 level. Specific different views regarding IR systems among different groups of users are described and discussed. Implications of the study for information science and IR system designs are suggested.