13928844
Unsupervised video-shot segmentation and model-free anchorperson detection for news video story parsing
News story parsing is an important and challenging task in a news video library system. We address two important components in a news video story parsing system: shot boundary detection and anchorperson detection. First, an unsupervised fuzzy c-means algorithm is used to detect video-shot boundaries in order to segment a news video into video shots. Then, a graph-theoretical cluster analysis algorithm is implemented to classify the video shots into anchorperson shots and news footage shots. Because of its unsupervised nature, the algorithms require little human intervention. The efficacy of the proposed method is extensively tested on more than five hours of news programs.
35059796
A Unified Framework for Shot Boundary Detection
According to drawbacks of available algorithms,a new hierarchical and multiresolution approach to the detection and classification of scene breaks in video sequences is presented in this paper. This method gives a unified framework for different shots by using the multi-resolution analysis as mainstay. Firstly, the video clips are cut by FCM clustering method, then the fade and dissolve are detected using ECR and SCD algorithm in the high components and the low components respectively by integer-to-integer wavelet transform. Finally, according to the cut and the former detection of gradual changes, we use motion vectors in high-components of 3D-WT to detect the wipe transition. Experimental results with real video clips about 7h demonstrate that our method can detect and classify a variety of scene breaks, including cuts, fades and dissolves, even in sequences involving significant motions and flash.
28211985
A Fully Unsupervised Image Segmentation Algorithm Based on Wavelet-Domain Hidden Markov Tree Models
A fully unsupervised image segmentation algorithm is presented in this paper, in which wavelet-domain hidden Markov tree (WD-HMT) model is exploited together with the cluster analysis and validity techniques. The true number of textures in a given image is determined by calculating the likelihood disparity of textures using the modified partition fuzzy degree (MPFD) function at one suitable scale. Then, possibilistic C-means (PCM) clustering is performed to determine the training sample data from different textures according to the true number of textures obtained. The unsupervised segmentation is changed into self-supervised one, and the HMTseg algorithm is used to achieve the final segmentation results. This algorithm is applied to segment a variety of composite texture images into distinct homogeneous regions and good segmentation results are reported.
8123307
Strategies anticipating a difference in search depth using opponent-model search
In this contribution we propose a class of strategies which focus on the game as well as on the opponent. Preference is given to the thoughts of the opponent, so that the strategy under investigation might be speculative. We describe a generalization of OM search, called (D, d)-OM search, where D stands for the depth of search by the player and d for the opponent's depth of search. A known difference in search depth can be exploited by purposely choosing a suboptimal variation with the aim to gain a larger advantage than when playing the objectively best move. The difference in search depth may have the result that the opponent does not see the variation in sufficiently deep detail. We then give a pruning alternative for (D, d)-OM search, denoted by α-β 2 pruning. A best-case analysis shows that α-β 2 prunes very efficiently, comparable to the efficiency of α-β with regard to minimax. The effectiveness of the proposed strategy is confirmed by simulations using a game-tree model including an opponent model and by experiments in the domain of Othello.
46154955
A Novel Typical-Sample-Weighted Clustering Algorithm for Large Data Sets
In the field of cluster analysis, most of existing algorithms are developed for small data sets, which cannot effectively process the large data sets encountered in data mining. Moreover, most clustering algorithms consider the contribution of each sample for classification uniformly. In fact, different samples should be of different contribution for clustering result. For this purpose, a novel typical-sample-weighted clustering algorithm is proposed for large data sets. By the atom clustering, the new algorithm extracts the typical samples to reduce the data amount. Then the extracted samples are weighted by their corresponding typicality and then clustered by the classical fuzzy c-means (FCM) algorithm. Finally, the Mahalanobis distance is employed to classify each original sample into obtained clusters. It is obvious that the novel algorithm can improve the speed and robustness of the traditional FCM algorithm. The experimental results with various test data sets illustrate the effectiveness of the proposed clustering algorithm.
2376280
A Radar Target Multi-feature Fusion Classifier Based on Rough Neural Network
Fusing Multi-feature will benefit radar target classification with much more belief. However, since radar target attributes such as high range resolution profiles, waveforms, frequency spectra, time-frequency spectra, higher order statistics, polarization spectra and flight path are of different dimensions, it is hard to make decision by fusing multi-feature directly. Fortunately, rough set makes decision by examining the fitness of each condition attribute separately, while neural network is powerful for dealing with nonlinear problems. With radial projection of target dimension, cruising velocity and height as condition attribute, a multi-feature rough neural network fusion classifier is presented. Simulation of the proposed classifier based on an information system with 25 targets belonging to 6 classes shows accuracy not less than 93%, while attributes are subjoined with typical radar errors.
28547876
Extraction of Sagittal Symmetry Planes from Pet Images
1923404
Global communication optimization for tensor contraction expressions under memory constraints
The accurate modeling of the electronic structure of atoms and molecules involves computationally intensive tensor contractions involving large multi-dimensional arrays. The efficient computation of complex tensor contractions usually requires the generation of temporary intermediate arrays. These intermediates could be extremely large, but they can often be generated and used in batches through appropriate loop fusion transformations. To optimize the performance of such computations on parallel computers, the total amount of inter-processor communication must be minimized, subject to the available memory on each processor In this paper we address the memory-constrained communication minimization problem in the context of this class of computations. Based on a framework that models the relationship between loop fusion and memory usage, we develop an approach to identify the best combination of loop fusion and data partitioning that minimizes inter-processor communication cost without exceeding the per-processor memory limit. The effectiveness of the developed optimization approach is demonstrated on a computation representative of a component used in quantum chemistry suites.
17022495
Improving Web clustering by cluster selection
Web page clustering is a technology that puts semantically related Web pages into groups and is useful for categorizing, organizing, and refining search results. When clustering using only textual information, suffix tree clustering (STC) outperforms other clustering algorithms by making use of phrases and allowing clusters to overlap. One problem of STC and other similar algorithms is how to select a small set of clusters to display to the user from a very large set of generated clusters. The cluster selection method used in STC is flawed in that it does not handle overlapping clusters appropriately. This paper introduces a new cluster scoring function and a new cluster selection algorithm to overcome the problems with overlapping clusters, which are combined with STC to make a new clustering algorithm ESTC. This paper's experiments show that ESTC significantly outperforms STC and that even with less data ESTC performs similarly to a commercial clustering search engine.
13249479
Fair and efficient router congestion control
Congestion is a natural phenomenon in any network queuing system, and is unavoidable if the queuing system is operated near capacity. In this paper we study how to set the rules of a queuing system so that all the users have a self-interest in controlling congestion when it happens.Routers in the internet respond to local congestion by dropping packets. But if packets are dropped indiscriminately, the effect can be to encourage senders to actually increase their transmission rates, worsening the congestion and destabilizing the system. Alternatively, and only slightly more preferably, the effect can be to arbitrarily let a few insistent senders take over most of the router capacity.We approach this problem from first principles: a router packet-dropping protocol is a mechanism that sets up a game between the senders, who are in turn competing for link capacity. Our task is to design this mechanism so that the game equilibrium is desirable: high total rate is achieved and is shared widely among all senders. In addition, equilibrium should be reestablished quickly in response to changes in transmission rates. Our solution is based upon auction theory: in principle, although not always in practice, we drop packets of the highest-rate sender, in case of congestion. We will prove the game-theoretic merits of our method. We'll also describe a variant of the method with some further advantages that will be supported by network simulations.
39682007
TreeWrapper: Automatic Data Extraction Based on Tree Representation
This paper introduces a new algorithm that learns to extract data from Web pages with relatively regular data structures. Current existing systems require training on either manually labelled pages or at least two similar unlabelled pages, and they often have difficulties on handling Web pages with complex formats such as nested tables or lists. Our previous system AutoWrapper does not need any training and can automatically extract data from any single page. This paper improves AutoWrapper by handling nested structures and finding multiple regular data areas. The main contributions include a tree-based representation for Web pages, an expressive language for representing information extraction patterns, and a learning algorithm that automatically detects regular data areas by finding similar sub-trees.