147466
A vision-based approach to early detection of drowning incidents in swimming pools
We present in this paper a vision-based approach to detection of drowning incidents in swimming pools at the earliest possible stage. The proposed approach consists of two main parts: a vision component which can reliably detect and track swimmers in spite of large scene variations of monitored pool areas, and an event-inference module which parses observation sequences of swimmer features for possible drowning behavioral signs. The vision component employs a model-based approach to represent and differentiate the background pool areas and foreground swimmers. The event-inference module is constructed based on a finite state machine, which integrates several reasoning rules formulated from universal motion characteristics of drowning swimmers. Possible drowning incidents are quickly detected using a sequential change detection algorithm. We have applied the proposed approach to a number of video clips of simulated drowning and obtained promising results as reported in this paper.
33005423
Unsupervised clustering of dominant scenes in sports video
We propose a new and efficient approach for clustering dominant scenes in sports video. To perform clustering in an unsupervised manner, we devise a recursive peer-group filtering (PGF) scheme to identify prototypical shots for each dominant scene, and examine time coverage of these prototypical shots to decide the number of dominant scenes for each sports video under analysis. To improve clustering efficiency, we employ principal component analysis and linear discriminant analysis to project high dimensional shot features to lower dimensional spaces suitable for classification. The main contribution of the paper lies in the formulation of clustering dominant scenes in sports video and the development of an efficient, unsupervised solution making use of PGF, time-coverage criterion, and subspace analysis. Experimental results obtained from various types of sports videos are presented to show the efficacy of the proposed approach.
28878840
Model-based clustering and analysis of video scenes
We make two contributions. First, we develop an unsupervised method to discover clusters of video scenes and summarize them with a concise Gaussian mixture model. To search for the best possible model, an effective procedure is devised to compare among models with different dimensions (i.e., numbers of mixture components) and, for a given dimension, among models with different parameters. Second, we propose a scene interference measure to characterize the interaction among different scenes of a video sequence. When applied to the clustered video scenes, the measure can reveal the dominant video segments of a class of videos without requiring much domain-specific knowledge. The proposed methods have been tested with a large number of sports videos and promising results are reported.
9891360
Image retrieval with SVM active learning embedding Euclidean search
Image retrieval with relevance feedback suffers from the small sample problem. Recently, SVM active learning has been proposed to tackle this problem, showing promising results. However, a small but sufficient number of initially labelled samples are still required to ensure subsequent efficient active learning and good retrieval performance. In the existing method, the user is asked to label more images before active learning starts. In this paper, a method of embedding Euclidean search into SVM active learning is proposed. With the help of Euclidean search, the adverse effect on retrieval performance due to lack of initially labelled samples can be reduced. Experimental results demonstrate the improvement by the proposed method, especially when the number of initially labelled samples is small.
34798576
Video transcoding for fast forward/reverse video playback
Fast forward and fast reverse playbacks are two common video browsing functions provided in many analog and digital video players. They help users quickly find and access video segments of interest by scanning through the content of a video at a faster than normal playback speed. We propose a video transcoding approach to realizing fast forward and reverse video playbacks by generating a new compressed video from a pre-coded video. To reduce the computational requirements, we design and compare several fast algorithms for estimating the motion vectors required in transcoded video. To accommodate changes due to frame skipping for fast video playback, we also alter the group-of-pictures structure of transcoded video. In addition, subjective tests are conducted to assess the minimum video peak-signal-to-noise-ratio degradation that is perceptible to viewers at different fast playback speeds. To this end, we obtain an adaptive video transcoding method, which combines intra-coding and inter-coding with a fast motion vector reestimation method to strike a good balance between computational complexity and transcoded video quality. Experimental results are reported to show the efficacy of the proposed method.
5556167
A color histogram based people tracking system
A system using color histogram based recognition technique is presented for tracking of moving people. The proposed system aims to resolve the identity of each tracked person after an occlusion, which is a common problem encountered in tracking of multiple objects. The system recognizes and keeps track of moving people with the aid of their color histograms. To make the recognition more robust against change of background illumination, a shadow-removal scheme is designed to select only those pixels belonging to the tracked people for constructing their color histograms. Experimental results of real video data are reported to show the efficacy of the proposed system.
21500667
Efficient block-matching motion estimation based on Integral frame attributes
Block-based motion estimation is widely used in video compression for exploiting video temporal redundancy. Although effective, the process is arguably the most computationally intensive part of a typical video encoder. To speed up the process, a large number of fast block-matching algorithms (BMAs) have been proposed for motion estimation by limiting the number of search locations or simplifying the measure of match between the two blocks under comparison. In this paper we propose a new BMA that measures the match by using such features as block sum and block variance, which can be easily computed using integral frame attributes. Experimental results show that the proposed BMA can reduce the computational complexity notably and achieve compression performance very close to that of existing BMAs using conventional block-matching measures.
181942
Search engine driven author disambiguation
In scholarly digital libraries, author disambiguation is an important task that attributes a scholarly work with specific authors. This is critical when individuals share the same name. We present an approach to this task that analyzes the results of automatically-crafted Web searches. A key observation is that pages from rare Web sites are stronger source of evidence than pages from common Web sites, which we model as inverse host frequency (IHF). Our system is able to achieve an average accuracy of 0.836
14371676
On the Formation of Peer-to-Peer Networks: Self-Organized Sharing and Groups
In this paper, we investigate the formation of peer-to-peer (P2P) networks with rational participating agents (active peers). In the absence of a central planner, peers choose their own utility-maximizing strategies for coalition and peer formation. P2P networks evolve dynamically through the activities of interactions among individual nodes and group units. We propose a framework for multilevel formation dynamics, including an individual level (content sharing decision and group selection) and a group level (membership admission). The respective utilities of the individual node and the collective player are formulated as functions of operational performance metrics such as expected content availability, search delay, transmission delay, and download delay. We study the impacts of various system parameters on the emergence of self-organized P2P network configuration features such as free-riding level and group size. Furthermore, we investigate the stability and efficiency of P2P networks and propose internal transfer mechanisms that force stable networks to become efficient.
27173472
Guest editorial
increasing relevance of security, safety and privacy issues 2 and of d the their perceived or subjective counterpartÈin IT and in particular on the web ; fact that trust is essential for any organization, or any human social d the relation, and thus should be maintained and created in new forms of computer supported collaboration and computer mediated communities ; fact that what is growing on the web is a new market, Electronic d the Commerce (EC), and since a market is mainly based on selÐsh interests, it is open to cheating and to competition between attractive alternatives. Hence, this new virtual market strongly requires trust in the chosen partner, trust in rules and procedures, trust in guaranties and enforcing authorities.
14496219
Optimal Processing Policies for an e-Commerce Web Server
The explosive growth in online shopping has provided online retailers impressive opportunities to extend revenue and profit. However, retailers may lose considerable online business from slow response times at electronic shopping sites. Although increasing server capacity may improve response time, the resources needed to do so are clearly not free. In this study, we propose a scheme that can improve a server's performance with no more than its current capacity. This scheme is based on priority processing, where the priority assigned to a customer depends on the potential revenue that is likely to be generated from the customer. The results for single-period analysis show that the benefit from priority processing increases as the server becomes more heavily utilized. 
 
We have also modeled a multiperiod version of the problem, where the demand in a period depends on the quality of service (QoS) that buyers receive in the previous period. In the multiperiod problem, both the server capacity and the processing policy in each period are determined optimally. Here, it may be optimal for the retailer to sacrifice profit and increase QoS in initial periods in order to increase demand (and revenue) in later periods. Thus, the processing policy of the server evolves from an emphasis on QoS in initial periods, to one on profit in subsequent periods.