Video summarization based on user log enhanced link analysis
Efficient video data management calls for intelligent video summarization tools that automatically generate concise video summaries for fast skimming and browsing. Traditional video summarization techniques are based on low-level feature analysis, which generally fails to capture the semantics of video content. Our vision is that users unintentionally embed their understanding of the video content in their interaction with computers. This valuable knowledge, which is difficult for computers to learn autonomously, can be utilized for video summarization process. In this paper, we present an intelligent video browsing and summarization system that utilizes previous viewers' browsing log to facilitate future viewers. Specifically, a novel ShotRank notion is proposed as a measure of the subjective interestingness and importance of each video shot. A ShotRank computation framework is constructed to seamlessly unify low-level video analysis and user browsing log mining. The resulting ShotRank is used to organize the presentation of video shots and generate video skims. Experimental results from user studies have strongly confirmed that ShotRank indeed represents the subjective notion of interestingness and importance of each video shot, and it significantly improves future viewers' browsing experience.

title: Internet-based interactive HDTV
abstract: Traditional interactive TV systems depend on expensive hardware, proprietary formats, and a closed-loop end-to-end approach, which greatly limits scalability and extensibility of TV services. In this paper, we present the HDControl interactive Internet TV architecture that achieves an open service model and combines high-quality video with flexible user control using two key software real-time algorithms: visual information embedding (VIE) algorithm and resynchronization algorithm. Experimental results in our HDTV testbed have confirmed the feasibility and efficiency of our proposed algorithms.

title: Adaptive wavelet thresholding for image denoising and compression
abstract: The first part of this paper proposes an adaptive, data-driven threshold for image denoising via wavelet soft-thresholding. The threshold is derived in a Bayesian framework, and the prior used on the wavelet coefficients is the generalized Gaussian distribution (GGD) widely used in image processing applications. The proposed threshold is simple and closed-form, and it is adaptive to each subband because it depends on data-driven estimates of the parameters. Experimental results show that the proposed method, called BayesShrink, is typically within 5% of the MSE of the best soft-thresholding benchmark with the image assumed known. It also outperforms SureShrink (Donoho and Johnstone 1994, 1995; Donoho 1995) most of the time. The second part of the paper attempts to further validate claims that lossy compression can be used for denoising. The BayesShrink threshold can aid in the parameter selection of a coder designed with the intention of denoising, and thus achieving simultaneous denoising and compression. Specifically, the zero-zone in the quantization step of compression is analogous to the threshold value in the thresholding function. The remaining coder design parameters are chosen based on a criterion derived from Rissanen's minimum description length (MDL) principle. Experiments show that this compression method does indeed remove noise significantly, especially for large noise power. However, it introduces quantization noise and should be used only if bitrate were an additional concern to denoising.

title: Convexity of Minimal Total Dominating Functions in Graphs
abstract: A total dominating function (TDF) of a graph G=(V, E) is a function f ∶ V → [0,1] such that for each v ∈ V, the sum of f values over all neighbours of ν (i.e., all vertices adjacent to v) is at least one. Integer-valued TDFs are precisely the characteristic functions of total dominating sets of G. A minimal TDF (MTDF) is one such that decreasing any value of it makes it non-TDF. An MTDF f is called universal if convex combinations of f and any other MTDF are minimal. We give a sufficient condition for an MTDF to be universal which generalizes previous results. Also we define a splitting operation on a graph G as follows: take any vertex ν in G and a vertex ω not in G and join ω with all the neighbours of v. A graph G has a universal MTDF if and only if the graph obtained by splitting G has a universal MTDF. A corollary is that graphs obtained by the operation from paths, cycles, complete graphs, wheels, and caterpillar graphs have a universal MTDF.

title: Synergistic Integration of Agent Technologies for Military Simulation
abstract: To perform large-scale coordination in real-world environments requires that many individually complex technologies come together to form integrated solutions. In this paper, we present an application where several key technologies are integrated into a unified system via a multiagent infrastructure. We show how the synergistic behavior among heterogeneous technologies results in a significant improvement over the performance of the individual technologies acting alone. Critical extensions were required to the language describing required behavior to allow the pieces to work together. Initial experimental results show system performance on a task of coordinating a military convoy in an adversarial environment was significantly improved when all technologies worked together. However, experiments with a human user in the loop showed that significant advances must still be made before such systems can be fielded in the real-world

title: Dynamic Splitting Policies of the Adaptive 3DR-Tree for Indexing Continuously Moving Objects
abstract: Moving-objects databases need a spatio-temporal indexing scheme for moving objects to efficiently process queries over continuously changing locations of the objects. A simple extension of the R-tree that employs time as the third dimension of the data universe shows low space utilization and poor search performance because of overlapping index regions. In this paper, we propose a variant of the 3-dimensional R-tree called the Adaptive 3DR-tree. The dynamic splitting policies of the Adaptive 3DR-tree significantly reduce the overlap rate, and this, in turn, results in improved query performance. The results of our extensive experiments show that the Adaptive 3DR-tree outperforms the original 3D R-tree and the TB-tree typically by a big margin.

title: Adaptive Query Processing in Point-Transformation Schemes
abstract: Point-transformation schemes transform multidimensional points to one-dimensional values so that conventional access methods can be used to index multidimensional points, enabling convenient integration of multidimensional query facilities into complex transactional database management systems. However, in high-dimensional data spaces, the computational complexity of the required query transformation is prohibitive. This paper proposes a near-optimal solution based on our novel ideas of adaptive Z-ordering. The experimental results show that the proposed approach significantly improves the query performance of point-transformation schemes. The idea can easily be generalized to accommodate any hierarchical space-filling curve, not just the Z-curve.