Efficient video similarity measurement with video signature
The video signature method has previously been proposed as a technique to summarize video efficiently for visual similarity measurements (see Cheung, S.-C. and Zakhor, A., Proc. SPIE, vol.3964, p.34-6, 2000; ICIP2000, vol.1, p.85-9, 2000; ICIP2001, vol.1, p.649-52, 2001). We now develop the necessary theoretical framework to analyze this method. We define our target video similarity measure based on the fraction of similar clusters shared between two video sequences. This measure is too computationally complex to be deployed in database applications. By considering this measure geometrically on the image feature space, we find that it can be approximated by the volume of the intersection between Voronoi cells of similar clusters. In the video signature method, sampling is used to estimate this volume. By choosing an appropriate distribution to generate samples, and ranking the samples based upon their distances to the boundary between Voronoi cells, we demonstrate that our target measure can be well approximated by the video signature method. Experimental results on a large dataset of Web video and a set of MPEG-7 test sequences with artificially generated similar versions are used to demonstrate the retrieval performance of our proposed techniques.

title: Fast similarity search on video signatures
abstract: Video signatures are compact representations of video sequences designed for efficient similarity measurement. In this paper, we propose a feature extraction technique to support fast similarity search on large databases of video signatures. Our proposed technique transforms the high dimensional video signatures into low dimensional vectors where similarity search can be efficiently performed. We exploit both the upper and lower bounds of the triangle inequalities in approximating the high-dimensional metric, and combine this approximation with the classical PCA to achieve the target dimension. Experimental results on a large set of Web video sequences show that our technique outperforms fastmap, Haar wavelet, PCA, and triangle-inequality pruning.

title: Fast similarity search and clustering of video sequences on the world-wide-web
abstract: We define similar video content as video sequences with almost identical content but possibly compressed at different qualities, reformatted to different sizes and frame-rates, undergone minor editing in either spatial or temporal domain, or summarized into keyframe sequences. Building a search engine to identify such similar content in the World-Wide Web requires: 1) robust video similarity measurements; 2) fast similarity search techniques on large databases; and 3) intuitive organization of search results. In a previous paper, we proposed a randomized technique called the video signature (ViSig) method for video similarity measurement. In this paper, we focus on the remaining two issues by proposing a feature extraction scheme for fast similarity search, and a clustering algorithm for identification of similar clusters. Similar to many other content-based methods, the ViSig method uses high-dimensional feature vectors to represent video. To warrant a fast response time for similarity searches on high dimensional vectors, we propose a novel nonlinear feature extraction scheme on arbitrary metric spaces that combines the triangle inequality with the classical Principal Component Analysis (PCA). We show experimentally that the proposed technique outperforms PCA, Fastmap, Triangle-Inequality Pruning, and Haar wavelet on signature data. To further improve retrieval performance, and provide better organization of similarity search results, we introduce a new graph-theoretical clustering algorithm on large databases of signatures. This algorithm treats all signatures as an abstract threshold graph, where the distance threshold is determined based on local data statistics. Similar clusters are then identified as highly connected regions in the graph. By measuring the retrieval performance against a ground-truth set, we show that our proposed algorithm outperforms simple thresholding, single-link and complete-link hierarchical clustering techniques.

title: Efficient video similarity measurement and search
abstract: We consider the use of meta-data and/or video-domain methods to detect similar videos on the Web. Meta-data is extracted from the textual and hyperlink information associated with each video clip. In the video domain, we apply an efficient similarity detection algorithm called video signature. The idea is to form a signature for each clip by selecting a small number of its frames that are most similar to a set of random seed images. We then apply a statistical pruning algorithm to allow fast detection on very large databases. Using a small ground-truth set, we achieve 90% recall and 95% precision using only 8% of the total number of operations required without pruning. For a database of around 46,000 video clips crawled from the Web, the video signature technique significantly outperforms meta-data in precision and recall. We show that even better performance can be achieved by combining them together. Based on our measurements, each video clip in our database has, on average, 1.53 similar copies.

title: Efficient video similarity measurement with video signature
abstract: The proliferation of video content on the Web makes similarity detection an indispensable tool in Web data management, searching, and navigation. We propose a number of algorithms to efficiently measure video similarity. We define video as a set of frames, which are represented as high dimensional vectors in a feature space. Our goal is to measure ideal video similarity (IVS), defined as the percentage of clusters of similar frames shared between two video sequences. Since IVS is too complex to be deployed in large database applications, we approximate it with Voronoi video similarity (VVS), defined as the volume of the intersection between Voronoi cells of similar clusters. We propose a class of randomized algorithms to estimate VVS by first summarizing each video with a small set of its sampled frames, called the video signature (ViSig), and then calculating the distances between corresponding frames from the two ViSigs. By generating samples with a probability distribution that describes the video statistics, and ranking them based upon their likelihood of making an error in the estimation, we show analytically that ViSig can provide an unbiased estimate of IVS. Experimental results on a large dataset of Web video and a set of MPEG-7 test sequences with artificially generated similar versions are provided to demonstrate the retrieval performance of our proposed techniques.

title: Alert-Driven Process Integration in a Web Services Environment

title: Stress Testing of Distributed Multimedia Software Systems
abstract: With the advance in network technologies, the use of distributed multimedia data has become very popular. Distributed multimedia systems are complex and error prone. While there are well established testing techniques for the functionalities of such systems, the technique of stress testing to improve test efficiency has not received much attention. It has been argued that faults are more likely to occur in situations where there is a fierce competition for resources. When the system is heavily loaded, errors like mismanagement of buffers can be easily identified. In this paper, we present several criteria for selecting test cases, and describe two methods for generating test cases which maximize system resource usage. Our approach is based on symbolic execution and constraint solving. An example is given to illustrate the concepts and techniques.

title: A metamodel based model transformation approach
abstract: The information transformation problem of model driven software development (MDSD) processes is gaining increasing attentions. In this paper, we present an indepth analysis of the major causes leading to the ambiguities of existing transformation. A metamodel based approach is then proposed to address these causes. A mechanism to generate transformation rules is devised based on a classification of the UML metamodel elements to be used in subsequent transformation processes. Some other critical issues relating to the generated transformation system are also discussed.

title: Tractable Flow Analysis for Anomaly Detection in Distributed Programs
abstract: Each process in a distributed program or design can be modelled as a process flow graph, where nodes represent program statements and directed edges represent control flows. This paper describes a flow analysis method to detect unreachable statements by examining the control flows and communication patterns in a collection of process flow graphs. The method can analyse programs with loops, non-deterministic structures and synchronous communication using an algorithm with a quadratic complexity in terms of program size. The method follows an approach described by Reif and Smolka  but delivers a more accurate result in assessing the reachability of statements. The higher accuracy is achieved using three techniques: statement dependency, history sets and statement re-reachability. The method is illustrated by a pump control application for a mining environment. A prototype has been implemented and its performance is presented.

title: Enhancing compositional reachability analysis with context constraints
abstract: Compositional techniques have been proposed for traditional reachability analysis in order to introduce modularity and to control the state explosion problem. While modularity has been achived, state explosion is still a problem. Indeed, this problem may even be exacerbated as a locally minimised subsystem may contain many states and transitions forbidden by its context or environments. This paper presents a method to alleviate this problem effectively by including context constraints in local subsystem minimisation. The global behaviour generated using the method is observationally equivalent to that generated by compositional reachability analysis without the inclusion of context constraints.Context constraints, specified as interface processes, are restrictions imposed by the environment on subsystem behaviour. The minimisation produces a simplified machine that describes the behaviour of the subsystem constrained by its context. This machine can also be used as a substitute for the original subsystem in the subsequent steps of the compositional reachability analysis. Interface processes capturing context constraints can be specified by users or automatically constructd using a simple algorithm. The concepts in the paper are illustrated with a clients/server system.