6333240
Web video topic discovery and tracking via bipartite graph reinforcement model
Automatic topic discovery and tracking on web-shared videos can greatly benefit both web service providers and end users. Most of current solutions of topic detection and tracking were done on news and cannot be directly applied on web videos, because the semantic information of web videos is much less than that of news videos. In this paper, we propose a bipartite graph model to address this issue. The bipartite graph represents the correlation between web videos and their keywords, and automatic topic discovery is achieved through two steps - coarse topic filtering and fine topic re-ranking. First, a weight-updating co-clustering algorithm is employed to filter out topic candidates at a coarse level. Then the videos on each topic are re-ranked by analyzing the link structures of the corresponding bipartite graph. After the topics are discovered, the interesting ones can also be tracked over a period of time using the same bipartite graph model. The key is to propagate the relevant scores and keywords from the videos of interests to other relevant ones through the bipartite graph links. Experimental results on real web videos from YouKu, a YouTube counterpart in China, demonstrate the effectiveness of the proposed methods. We report very promising results.
22546600
Fast and robust detection of near-duplicates in web video database
Web video databases tend to contain tremendous near-duplicates with the explosive growth of online videos. How to detect and eliminate these near-duplicates has become an essential problem for Web video storage and indexing. In this paper, we propose a hierarchical approach to solve this problem efficiently and effectively. For an incoming video, firstly we compare global features to exclude videos with large differences. Then pairwise comparison of key-frame level features with more computation cost is performed. For pairwise comparison, we adopt the maximum matching (MM) framework on bipartite graph. Instead of using standard well-known MM algorithm, a simple approximate algorithm of MM and a graph cut algorithm are applied for a higher speed. Experimental results over two databases summing up to more than 20,000 videos demonstrate our algorithmpsilas excellent performance in both speed and accuracy.
449203
Topic mining on web-shared videos
Internet videos have grown exponentially with the help from video sharing Websites. Automatic topic mining is therefore increasingly important for organizing and navigating such large video databases. Most of current solutions of topic detection and mining were done on news videos and cannot be directly applied on Web videos, because of their limited and noisy semantic information. In this paper, we will try to address this problem and propose an automatic topic mining framework on Web videos. We develop an iterative weight-updated co-clustering scheme to filter "noisy" tags and mine the "hot" topics. We then propose a visual-based clustering approach to further group the videos with similar content, and rank the visual-similar groups by their similarity to the topic center. Experiments on a large Web video database demonstrate the superior performance of our weight-updated co-clustering to both of the traditional co-clustering and k-means. The experiments also demonstrate significant improvement of users' experience by our visual-based clustering and ranking.
17111160
Video Histogram: A Novel Video Signature for Efficient Web Video Duplicate Detection
The explosive growth of information technology and digital content industry stimulates various video applications over the Internet. Since it is quite easy to copy, reformat, modify and republish video files on the websites, similarity/duplicate detection and measurement is essential to identify the excessive content duplication, so as to facilitate effective video search and intelligence propriety protection as well. In this paper, we propose a novel signature-based approach for duplicate video comparison. The so-called video histogram scheme counts the numbers of video’s frames that are closest to a set of representative seed vectors chosen from the feature space of the training data set in advance. Then all the numbers are normalized to generate the signature of the video for further comparison. As our signature is a compact fixed-size vector with low dimension for each video, it requires less storage and computation cost than previous methods. The experiments show that our approach is both efficient and effective for web video duplicate detection.
14971936
Does Familiarity Breed Content? Taking Account of Familiarity with a Topic in Personalizing Information Retrieval
We report on an evaluation of the effectiveness of considering a user's familiarity with a topic in improving information retrieval performance. This approach to personalization is based on previous results indicating differences in user search behavior and judgments according to his/her familiarity to the topic explored, and to research on using implicit sources of evidence to determine the user's context and preferences. Our attempt was to relate a topic-dependent concept and measure, familiarity with the topic, with topic-independent measures of documents such as readability, concreteness/abstractness, and specificity/generality. Contrary to our expectations, a user’s familiarity with a topic has no effect on the utility of readability or concrete/abstract scoring. We are encouraged, however, to find that high readability had a positive effect on search results, regardless of a user’s familiarity with a topic.
8904843
Social Peer-to-Peer for Resource Discovery
For resource discovery in social networks, people can directly contact some acquaintances that have knowledge about the resources they are looking for. However, in current peer-to-peer networks, peer nodes lack capabilities similar to social networks, making it difficult to route queries efficiently. In this paper, we present a social-like system (Social-P2P) for resource discovery by mimicking human behaviours in social networks. Different from most informed search algorithms, peer nodes learn knowledge from the results of previous searches and no additional overhead is required to obtain extra information from neighbouring nodes. Unlike community-based P2P information sharing systems, we do not intend to create and maintain peer groups or communities consciously. Peer nodes with the same interests will be highly connected to each other spontaneously. Social-P2P has been simulated in a dynamic environment. From the simulation results and analysis, Social-P2P achieved better performance than current methods
2746167
Modelling and simulation of Network Enabled Capability on service-oriented architecture
Network Enabled Capability (NEC) is the UK Ministry of Defence’s response to the quickly changing conflict environment in which its forces must operate. In NEC, systems need to be integrated in context, to assist in human activity and provide dependable inter-operation. In this paper, we present our research work in the NECTISE project with a focus on the modelling and simulation of service-oriented architecture (SOA) for delivering dependable and sustainable military capability. The simulation results indicate that the proposed architectural model can provide a high-level of reliability and sustainability in the provision of capability in a dynamic environment. Moreover, a NEC demonstration system for regional surveillance is introduced in this paper to illustrate the use of SOA to achieve NEC.
36508955
Parameter convergence and minimal internal model with an adaptive output regulation problem
The parameter convergence of nonlinear adaptive control systems is an important yet not well addressed issue. In this paper, using the global robust output regulation problem of output feedback systems with unknown exosystems as a platform, we will show that the well known persistency of excitation (PE) condition still guarantees the convergence of the estimated parameter vector to the true parameter vector. Moreover, the PE condition will be satisfied if the internal model is minimal in certain sense.
5384230
A Simulation Platform for Intelligent Scheduling In Automated Container Terminal
The present invention relates generally to a method and apparatus for evaluating the resistivity of invaded formations at high apparent dip angle. A multi-array induction tool having a plurality of arrays is disposed in a borehole. Signals from a subset of coils of the array are selected to probe different volumes of the formation surrounding the borehole. The maximum entropy method is used to process data from the subset of coils to effectively remove distortions produced by shoulder and dip effect in the presence of shallow, moderate, or deep invasion. An advantage of the invention is a computer program that accurately predicts the response of induction arrays in an assumed layered formation with dip. After processing the data, the resulting multi-array induction log will indicate a conductivity profile for the subset of coils which is substantially identical to that of an array in a thick bed, without dip or layering, with the same invasion profile. A 1D radial inversion of the resulting logs at each point in depth will give a closer estimate of the actual formation parameters, Rxo, Rt, and the invasion radius.