Web object indexing using domain knowledge
A web object is defined to represent any meaningful object embedded in web pages (e.g. images, music) or pointed to by hyperlinks (e.g. downloadable files). In many cases, users would like to search for information of a certain 'object', rather than a web page containing the query terms. To facilitate web object searching and organizing, in this paper, we propose a novel approach to web object indexing, by discovering its inherent structure information with existed domain knowledge. In our approach, first, Layered LSI spaces are built for a better representation of the hierarchically structured domain knowledge, in order to emphasize the specific semantics and term space in each layer of the domain knowledge. Meanwhile, the web object representation is constructed by hyperlink analysis, and further pruned to remove the noises. Then an optimal matching between the web object and the domain knowledge is performed, in order to pick out the structure attributes of the web object from the knowledge. Finally, the obtained structure attributes are used to re-organize and index the web objects. Our approach also indicates a new promising way to use trust-worthy Deep Web knowledge to help organize dispersive information of Surface Web.

title: Dominant Feature Vectors Based Audio Similarity Measure
abstract: This paper presents an approach to extracting dominant feature vectors from an individual audio clip and then proposes a new similarity measure based on the dominant feature vectors. Instead of using the mean and standard deviation of frame features in most conventional methods, the most salient characteristics of an audio clip are represented in the form of several dominant feature vectors. These dominant feature vectors give a better description of the fundamental properties of an audio clip, especially when frame features change a lot along the time line. Evaluations on a content-based audio retrieval system indicate an obvious improvement after using the proposed similarity measure, compared with some other conventional methods.

title: Automatic music video generation based on temporal pattern analysis
abstract: Music video (MV) is a short film meant to present a visual representation of a popular music song. In this paper, we present a system that automatically generates MV-like videos from personal home videos based on observations that generally there are obvious repetitive visual and aural patterns in MVs. Based on a set of video and music analysis algorithms, the automatic music video (AMV) generation system automatically extracts temporal structures of the video and music, as well as repetitive patterns in the music. And then, according to the structure and patterns, a set of highlight segments from the raw home video footage are selected, aiming at matching the visual content with the aural structure and pattern. And last, the output music video is rendered by connecting the selected highlight video segments with appropriate transition effects, accompanied with the music. Experiments show that the results are compelling and promising.

title: Repeating pattern discovery from acoustic musical signals
abstract: Music pieces are typically repetitive. The automatic extraction of repeating patterns is useful for music summary, indexing and retrieval. An effective approach for repeating pattern discovery is proposed. In order to represent the melody similarity more accurately, a constant Q transform is used for feature extraction and a novel similarity measure between musical features is proposed. From the self-similarity matrix of the music, an adaptive method is used to extract all the significant repeating patterns. Experiments on pop music indicate the approach is promising.

title: Universal Background Models for Real-time Speaker Change Detection
abstract: This paper addresses the problem of real-time speaker change detection in TV news broadcast, in which no prior knowledge on speakers is assumed. To remove the unreliable frames and background frames in the speech stream, we propose a new approach for feature categorization based on Gaussian Mixture Model - Universal Background Model (GMM-UBM). The feature vectors are categorized into three sets, which include reliable speech, doubtful speech and unreliable speech. Then a novel distance measure is presented correspondingly for real-time speaker change detection. Extensive experiments demonstrate its good performance, and intrinsic difficulties on real-time speaker change detection are discussed as well in this paper.

title: Real-time unsupervised speaker change detection
abstract: Information on speaker change point is very useful for speaker tracking and other applications. In this paper, we presented an effective algorithm for automatic speaker change detection based on LSP correlation analysis. Moreover, a general case is considered, in which the speaker and speaker number are both assumed unknown. The algorithm has low complexity and can be processed in real-time with a limit delay. Our experiments have shown that the algorithms produce very satisfactory results.

title: The diameter of random massive graphs
abstract: Many massive graphs (such as the WWW graph and Call graphs) share certain universal characteristics which can be described by so-called the “power law”. Here we determine the diameter of random power law graphs up to a constant factor for almost all ranges of parameters. These results show a strong evidence that the diameters of most massive graphs are about logarithm of their sizes up to a constant factor.

title: Constructive Negation Using Typed Existence Properties
abstract: A method for extracting positive information from negative goals is proposed. The method makes use of knowledge about the existence of solutions of predicates and about the types of variables. It strictly generalises earlier work in this area. It can be used as a construction rule or as a simplification rule. As a construction rule it does not involve an SLD-derivation of the negated sub-goal and so is applicable during compilation. As a simplification rule it prunes unsatisfiable goals without doing an explicit satisfiability test.

title: Constrained planar motion analysis by decomposition
abstract: General SFM methods give poor results for images captured by constrained motions such as planar motion. In this paper, we propose new SFM algorithms for images captured under a common but constrained planar motion: the image plane is perpendicular to the motion plane. We show that a 2D image captured under such constrained planar motion can be decoupled into two 1D images: one 1D projective and one 1D affine. We then introduce the 1D affine camera model for completing 1D camera models. Next, we describe new subspace reconstruction methods, and apply these methods to the images captured by concentric mosaics, which undergo a special case of constrained planar motion. Finally, we demonstrate both in theory and experiments the advantage of the decomposition method over the general SFM methods by incorporating the constrained motion into the earliest stage of motion analysis.

title: A Three Tiered Approach for Articulated Object Action Modeling and Recognition
abstract: Visual action recognition is an important problem in computer vision. In this paper, we propose a new method to probabilistically model and recognize actions of articulated objects, such as hand or body gestures, in image sequences. Our method consists of three levels of representation. At the low level, we first extract a feature vector invariant to scale and in-plane rotation by using the Fourier transform of a circular spatial histogram. Then, spectral partitioning [20] is utilized to obtain an initial clustering; this clustering is then refined using a temporal smoothness constraint. Gaussian mixture model (GMM) based clustering and density estimation in the subspace of linear discriminant analysis (LDA) are then applied to thousands of image feature vectors to obtain an intermediate level representation. Finally, at the high level we build a temporal multi-resolution histogram model for each action by aggregating the clustering weights of sampled images belonging to that action. We discuss how this high level representation can be extended to achieve temporal scaling in-variance and to include Bi-gram or Multi-gram transition information. Both image clustering and action recognition/segmentation results are given to show the validity of our three tiered representation.

title: Efficient particle filtering using RANSAC with application to 3D face tracking
abstract: Particle filtering is a very popular technique for sequential state estimation. However, in high-dimensional cases where the state dynamics are complex or poorly modeled, thousands of particles are usually required for real applications. This paper presents a hybrid sampling solution that combines RANSAC and particle filtering. In this approach, RANSAC provides proposal particles that, with high probability, represent the observation likelihood. Both conditionally independent RANSAC sampling and boosting-like conditionally dependent RANSAC sampling are explored. We show that the use of RANSAC-guided sampling reduces the necessary number of particles to dozens for a full 3D tracking problem. This method is particularly advantageous when state dynamics are poorly modeled. We show empirically that the sampling efficiency (in terms of likelihood) is much higher with the use of RANSAC. The algorithm has been applied to the problem of 3D face pose tracking with changing expression. We demonstrate the validity of our approach with several video sequences acquired in an unstructured environment.