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.
22284885
Motion vector re-estimation for fractional-scale video transcoding
In video transcoding, the compressed video is often converted to a lower resolution in order to reduce the bit rate. One way to achieve this is to first decompress the compressed video, down-sample each frame, and then re-compress the down-scaled frame sequences. This method can obtain high-quality compressed video bit-streams; however, it involves motion vector estimation that is computationally complex. Recently, some transcoding techniques have been proposed to compute the motion vectors for a compressed video with lower resolution using the motion vectors directly from the original compressed video. These techniques, however, cannot be used in arbitrary-size video down-scaling, e.g for converting CIF (Common Intermediate Format) video to sub-QCIF (Quarter CIF) video or QCIF to sub-QCIF. In this paper, we propose a video transcoding method that can estimate the motion vectors for arbitrary-size down-scaled video. It can provide video quality that is comparable to full-search motion estimation but with very low computational complexity.
27733167
An unsupervised approach to dominant video scene clustering
In this paper, we propose an unsupervised approach for dominant scene clustering in sports video. By adopting a customized peer group filtering (PGF) to identify prototypes for k-means clustering, dominant scenes can be clustered based on shot color histogram (SCH). Meanwhile, the number of clusters can automatically be determined by estimating the time coverage of dominant scenes. To improve the computational efficiency and clustering accuracy, principal component analysis (PCA) and linear discriminant analysis (LDA) are used to project SCH into reduced dimensional spaces. The prototypes obtained by PGF can also be served as sufficient and representative training data for LDA. Such good training data ensures LDA outperforms PCA with better clustering performance in more reduced feature dimension.
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.
32434128
Fast motion vector and bitrate re-estimation for arbitrary downsizing video transcoding
In video transcoding technique, many fast methods have been proposed to estimate the motion vectors required for downsizing a pre-coded video by an integer factor. In this paper we examine and compare several fast video transcoding methods for downsizing a pre-coded video by an arbitrary factor. Methods which out-perform others are identified and discussed. We also propose a new method to model the relation between bitrate and frame size under low bitrate condition. With this new model, we can re-estimate the bitrate according to a required frame size or select a suitable frame size for a given target bitrate, while maintaining good video quality. Experimental results are presented to show the performance of the proposed fast motion vector and bitrate re-estimation methods.
16080551
Institutional trust and familiarity in online interorganizational relationships
Institutional trust is necessary for inter-organizational relationships in e-commerce. Two types of institutional trust are herein proposed (a) third-party institution-based trust, which deals with intermediary-driven structures, and (b) bilateral institutionalized trust, which deals with dyadic inter-organizational processes. We examine how these two types of institutional trust develop through their underlying dimensions (i) structural assurances, (ii) facilitating conditions, and (iii) situational normality. We focus on how interorganizational trust and online relationship building can be longitudinally facilitated by institutional trust, which can initially substitute and progressively complement the role of familiarity. More specifically, this study focuses on the time-dependent nature of initial trust formation, and specifically on the role of relationship phase (from exploration to maturity), by describing (a) the role of bilateral relative to third-party institutional trust on interorganizational trust, and (b) the transition of the underlying dimensions of institutional trust. This research has implications for (1) the longitudinal nature and role of institutional trust in online relationship building, (2) the literature on initial trust formation, and (3) the strategic design and use of cost-effective trust-building mechanisms.
46672294
Guest editorial
Why model, analyze and build trust in the context of Agent-based systems? : An additional argument. One of the more interesting and relevant characteristics of the agent paradigm is the so-called autonomous behavior of the agent. We can say that almost each deÐnition of agent includes this concept of autonomy ; even if not always in the literature, this characteristic is so well deÐned and completely shared. In particular, an autonomous agent acts without the direct and complete intervention or control of any other agent (either user or software). More precisely, an autonomous agent can independently look for and acquire information, react to local contexts and events, have an evolving internal state, learn or evolve, and decide how and with whom to achieve a task that is not completely speciÐed. In general, when an agent has to collaborate with other agents to obtain its own goals (and this is in fact necessary for software agents that have limited resources and abilities, and operate in open and uncertain worlds), it should allocate (and receive allocation of) tasks to or by other agents. In sum, the characterization of an agent as autonomous and its need for collaboration with other agents to realize its own plans is one of the main reasons for introducing the notion of trust, analyzing its internal components, and adapting it to the various collaborative contexts. In this sense, to build an adequate model of trust would allow an agent (user) to evaluate
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.
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.
30616381
Constructive Default Logic and the Control of defeasible reasoning