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.
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.
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.
27936359
A new content-based hybrid video transcoding method
Many video transcoding architectures have been proposed to reduce the bitrates of compressed video bitstreams. Requantization of transformed coefficients (such as DCT coefficients), spatial resolution down-sampling, and temporal resolution down-sampling are the three transcoding tools which are commonly used to reduce compressed video bitrates. We propose a new video transcoding method that combines the use of different transcoding tools to transcode compressed video bit streams to various target bitrates while maintaining good subjective/objective video quality. The transcoding tools are selected based on the video content, which is characterized by two video content descriptors inferring the video spatial and motion activities, respectively. These two content descriptors can be readily computed from the compressed video bit streams without full decompression. Experimental results show that the proposed method can provide better video quality than other existing transcoders that make use of a single transcoding tool.
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.
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.
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.
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
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.
20191236
Optimal Rebating Strategies in Peer-to-Peer Content Distribution
Developed the algorithms that were the firm's core Intellectual Property (IP) at founding.