In this paper, we present a novel approach for efficient search of high-dimensional databases, such as video shots. The idea is to map feature vectors from the high-dimensional feature space into a point in a low-dimensional distance space. Then, a spatial access method, such as an R-tree, is used to cluster these points based on their distances in the low-dimensional space. Our mapping method, called topological mapping, guarantees no false dismissals in the result of a query. However, the result of a query might contain some false alarms. Hence, two refinement steps are performed to remove these false alarms. Comparative experiments on a database of video shots show the superior efficiency of the topological mapping method over other known methods.

It has been known that the cell loss ratio (CLR) characteristics of the multiplexed traffic depend on the arrangement of I-picture starting times of individual variable bit rate (VBR) MPEG video sources. In this paper, we propose a simple yet accurate traffic model for the multiplexed VBR MPEG video to calculate the CLR at an ATM multiplexer when the arrangement of the I-picture starting times of individual sources is given. In the proposed model, in order to represent the arrangement of the I-picture starting times, each picture type (I-, P-, or B-picture) of individual source is modeled by the arrival rate histogram, and the multiplexed video traffic is modeled by the convolution of the arrival rate histograms of the pictures that comprise the multiplexed traffic. Using the proposed traffic model, we propose an analytical method to calculate the CLR of the multiplexed VBR MPEG video at an ATM multiplexer. Simulation results show that the proposed method can calculate the CLR more precisely and efficiently than other existing methods.

Recently, two approaches investigated indexing and retrieving videos. One approach utilized the visual features of individual objects, and the other approach exploited the spatio-temporal relationships between multiple objects. In this paper, we integrate both approaches into a new video model, called the Visual-Spatio-Temporal (VST) model to represent videos. The visual features are modeled in a topological approach and integrated with the spatio-temporal relationships. As a result, we defined rich sets of VST relationships which support and simplify the formulation of more semantical queries. An intuitive query interface which allows users to describe VST features of video objects by sketch and feature specification is presented. The conducted experiments prove the effectiveness of modeling and querying videos by the visual features of individual objects and the VST relationships between multiple objects.

Video service is slated to be a major application of emerging high-speed communications networks of the future. In particular, full-motion video is designed to take advantage of the high bandwidths that will become affordably available with the advent of B-ISDN. A salient feature of compressed video sources is that they give rise to autocorrelated traffic streams, which are difficult to model with traditional modeling techniques. In this paper, we describe a new methodology, called TES (Transform-Expand-Sample) , for modeling general autocorrelated time series, and we apply it to traffic modeling of compressed video. The main characteristic of this methodology is that it can model an arbitrary marginal distribution and approximate the autocorrelation structure of an empirical sample such as traffic measurements. Furthermore, the empirical marginal (histogram) and leading autocorrelations are captured simultaneously. Practical TES modeling is computationally intensive and is effectively carried out with software support. A computerized modeling environment, called TEStool, is briefly reviewed. TEStool supports a heuristic search approach for fitting a TES model to empirical time series. Finally, we exemplify our approach by two examples of TES video source models, constructed from empirical codec bitrate measurements: one at the frame level and the other at the group-of-block level. The examples demonstrate the efficacy of the TES modeling methodology and the TEStool modeling environment.