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.
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.
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.
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.
31922506
Layering-based color filter array interpolation
This paper presents a new method to interpolate the color filter array (CFA) pattern that is commonly used in a single-sensor digital camera. The proposed method involves two main steps: slicing the color planes into layers to identify smooth local image regions; estimating the missing color values based on the spectral correlation between color planes. The performance of the proposed algorithm is evaluated by comparing the results with those generated by other existing methods. It shows that the proposed method outperforms other methods in terms of both subjective and objective image quality.
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.
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.
31919378
A risk management of ASPs
Application service providers (ASPs) provide access to application software over wide area networks and have been viewed as a means of making the benefits of outsourcing available to small businesses. However, many enterprise ASPs get the majority of their revenue from very large businesses while many vertical niche ASPs get their revenue from small businesses. We analyze this seeming anomaly from a risk management perspective. ASPs present a greater risk management problem to would-be customers than traditional outsourcing. We analyze the nature of the risk differences and then propose a model of how would-be customers process those risks. In our model, prospects first assess whether they can tolerate the worst case scenario and if they are able to do due diligence on the likely outcome. They consider benefits only if one of the previous assertions is true. The worst case scenario for enterprise applications is truly dire and only technologically sophisticated businesses are better able to identify the true risk profile. For vertical niche ASPs the worst case scenario is relatively mild, but economies of scale make the benefits relatively more appealing to small businesses.
16126034
Color filter array demosaicking: new method and performance measures
Single-sensor digital cameras capture imagery by covering the sensor surface with a color filter array (CFA) such that each sensor pixel only samples one of three primary color values. To render a full-color image, an interpolation process, commonly referred to as CFA demosaicking, is required to estimate the other two missing color values at each pixel. In this paper, we present two contributions to the CFA demosaicking: a new and improved CFA demosaicking method for producing high quality color images and new image measures for quantifying the performance of demosaicking methods. The proposed demosaicking method consists of two successive steps: an interpolation step that estimates missing color values by exploiting spatial and spectral correlations among neighboring pixels, and a post-processing step that suppresses noticeable demosaicking artifacts by adaptive median filtering. Moreover, in recognition of the limitations of current image measures, we propose two types of image measures to quantify the performance of different demosaicking methods; the first type evaluates the fidelity of demosaicked images by computing the peak signal-to-noise ratio and CIELAB DeltaE(*)(ab) for edge and smooth regions separately, and the second type accounts for one major demosaicking artifact-zipper effect. We gauge the proposed demosaicking method and image measures using several existing methods as benchmarks, and demonstrate their efficacy using a variety of test images.