17605322
Motion Activity Based Semantic Video Similarity Retrieval
Semantic feature extraction of video shots and fast video sequence matching are important and required for efficient retrieval in a large video database. In this paper, a novel mechanism of similarity retrieval is proposed. Similarity measure between video sequences considering the spatio-temporal variation through consecutive frames is presented. For bridging the semantic gap between low-level features and the rich meaning that users desire to capture, video shots are analyzed and characterized by the high-level feature of motion activity in compressed domain. The extracted features of motion activity are further described by the 2D-histogram that is sensitive to the spatio-temporal variation of moving objects. In order to reduce the dimensions of feature vector space in sequence matching, Discrete Cosine Transform (DCT) is exploited to map semantic features of consecutive frames to the frequency domain while retains the discriminatory information and preserves the Euclidean distance between feature vectors. Experiments are performed on MPEG-7 testing videos, and the results of sequence matching show that a few DCT transformed coefficients are adequate and thus reveal the effectiveness of the proposed mechanism of video retrieval.
5119403
Multimedia Information Systems
This is the third year for the minitrack on Multimedia Information Systems at the Annual Hawaii International Conference on System Sciences. The presence of this minitrack reflects the importance and recent proliferation of multimedia applications. This proliferation has been driven by technological advances resulting in dramatic decreases in price/performance ratios in processing and storage technologies, video and audio input/output devices, imaging systems, and telecommunications. As it is not disputed that multimedia technology will be an indispensable aspect of computer/human interaction in the next-generation computing environment, approaches for applying multi-media technology and mechanisms for managing multimedia information in organizations are in demand.
14165227
Special issue: "Web retrieval and mining"
53306841
Dental Biometrics: Alignment and Matching of Dental Radiographs
Dental biometrics utilizes dental radiographs for human identification. The dental radiographs provide information about teeth, including tooth contours, relative positions of neighboring teeth, and shapes of the dental work (e.g., crowns, fillings, and bridges). The proposed system has two main stages: feature extraction and matching. The feature extraction stage uses anisotropic diffusion to enhance the images and a mixture of Gaussians model to segment the dental work. The matching stage has three sequential steps: tooth-level matching, computation of image distances, and subject identification. In the tooth-level matching step, tooth contours are matched using a shape registration method and the dental work is matched on overlapping areas. The distance between the tooth contours and the distance between the dental works are then combined using posterior probabilities. In the second step, the tooth correspondences between the given query (postmortem) radiograph and the database (antemortem) radiograph are established. A distance based on the corresponding teeth is then used to measure the similarity between the two radiographs. Finally, all the distances between the given postmortem radiographs and the antemortem radiographs that provide candidate identities are combined to establish the identity of the subject associated with the postmortem radiographs.
14259579
FTXI: fault tolerance XCS in integer
In the realm of data mining, several key issues exists in the traditional classification algorithms, such as low readability, large rule number, and low accuracy with information losing. In this paper, we propose a new classification methodology, called fault tolerance XCS in integer (FTXI), by extending XCS to handle conditions in integers and integrating the mechanism of fault tolerance in the context of data mining into the framework of XCS. We also design and generate appropriate artificial data sets for examining and verifying the proposed method. Our experiments indicate that FTXI can provide the least rule number, obtain high prediction accuracy, and offer rule readability, compared to C4.5 and XCS in integer without fault tolerance.
9059456
Model Checking One Million Lines of C Code
Implementation bugs in security-critical software are pervasive. Several authors have previously suggested model checking as a promising means to detect improper use of system interfaces and thereby detect a broad class of security vulnerabilities. In this paper, we report on our practical experience using MOPS, a tool for software model checking security-critical applications. As examples of security vulnerabilities that can be analyzed using model checking, we pick five important classes of vulnerabilities and show how to codify them as temporal safety properties, and then we describe the results of checking them on several significant Unix applications using MOPS. After analyzing over one million lines of code, we found more than a dozen new security weaknesses in important, widely-deployed applications. This demonstrates for the first time that model checking is practical and useful for detecting security weaknesses at large scale in real,
27175112
Learnability of Relatively Quantified Generalized Formulas
In this paper we study the learning complexity of a vast class of quantifed formulas called Relatively Quantified Generalized Formulas. This class of formulas is parameterized by a set of predicates, called a basis. We give a complete classification theorem, showing that every basis gives rise to quantified formulas that are either polynomially learnable with equivalence queries, or not polynomially predictable with membership queries under some cryptographic assumption. We also provide a simple criteria distinguishing the learnable cases from the non-learnable cases.
6987127
3D model-assisted face recognition in video
Face recognition in video has gained wide attention as a covert method for surveillance to enhance security in a variety of application domains (e.g., airports). A video contains temporal information as well as multiple instances of a face, so it is expected to lead to better face recognition performance compared to still face images. However, faces appearing in a video have substantial variations in pose and lighting. These pose and lighting variations can be effectively modeled using 3D face models. Combining the advantages of 2D video and 3D face models, we propose a face recognition system that identifies faces in a video. The system utilizes the rich information in a video and overcomes the pose and lighting variations using 3D face model. The description of the proposed method and preliminary results are provided.