News video classification using SVM-based multimodal classifiers and combination strategies
Video classification is the first step toward multimedia content understanding. When video is classified into conceptual categories, it is usually desirable to combine evidence from multiple modalities. However, combination strategies in previous studies were usually ad hoc. We investigate a meta-classification combination strategy using Support Vector Machine, and compare it with probability-based strategies. Text features from closed-captions and visual features from images are combined to classify broadcast news video. The experimental results show that combining multimodal classifiers can significantly improve recall and precision, and our meta-classification strategy gives better precision than the approach of taking the product of the posterior probabilities.

title: Identifying Perspectives at the Document and Sentence Levels Using Statistical Models
abstract: In this paper we investigate the problem of identifying the perspective from which a document was written. By perspective we mean a point of view, for example, from the perspective of Democrats or Republicans. Can computers learn to identify the perspective of a document? Furthermore, can computers identify which sentences in a document strongly convey a particular perspective? We develop statistical models to capture how perspectives are expressed at the document and sentence levels, and evaluate the proposed models on a collection of articles on the Israeli-Palestinian conflict. The results show that the statistical models can successfully learn how perspectives are reflected in word usage and identify the perspective of a document with very high accuracy.

title: Web image retrieval re-ranking with relevance model
abstract: Web image retrieval is a challenging task that requires efforts from image processing, link structure analysis, and Web text retrieval. Since content-based image retrieval is still considered very difficult, most current large-scale Web image search engines exploit text and link structure to "understand" the content of the Web images. However, local text information, such as caption, filenames and adjacent text, is not always reliable and informative. Therefore, global information should be taken into account when a Web image retrieval system makes relevance judgment. We propose a re-ranking method to improve Web image retrieval by reordering the images retrieved from an image search engine. The re-ranking process is based on a relevance model, which is a probabilistic model that evaluates the relevance of the HTML document linking to the image, and assigns a probability of relevance. The experiment results showed that the re-ranked image retrieval achieved better performance than original Web image retrieval, suggesting the effectiveness of the re-ranking method. The relevance model is learned from the Internet without preparing any training data and independent of the underlying algorithm of the image search engines. The re-ranking process should be applicable to any image search engines with little effort.

title: A wearable digital library of personal conversations
abstract: We have developed a wearable, personalized digital library system, which unobtrusively records the wearer's part of a conversation, recognizes the face of the current dialog partner and remembers his/her voice. The next time the system sees the same person and hears the same voice, it can replay parts of the last conversation in compressed form. Results from a prototype system show the effectiveness of combining of face recognition and speaker identification for retrieving conversations.

title: Self-organizing fuzzy control of multi-variable systems using learning vector quantization network
abstract: Using learning vector quantization (LVQ) network to construct a self-organizing fuzzy controller (SOFC) for multi-variable nonlinear composite systems is developed in this paper. The LVQ network is used to provide information about the better locations of the IF-part membership functions through un-supervised learning. The generated fuzzy rule base is applied to the SOFC and updated by a self-learning procedure. Using Lyapunov stability methods, the proposed adaptive scheme is proven to provide the SOFC some degree of robust properties and guarantee uniform ultimate boundedness in the presence of disturbances, measurement noise and perturbed initialization error. The effectiveness of the proposed controller has been demonstrated numerically by applying to control a two-link manipulator.

title: Communication-Efficient Vector Manipulations on Binary N-Cubes
abstract: One way to find closest pairs in large datasets is to use hash functions , . In recent years locality-se nsitive hash functions for various metrics have been given: project ing an n-cube ontok bits is simple hash function that performs well. In this paper we investigate alternatives to projection. For various parameters hash functions given by complete decodi ng algorithms for codes work better, and asymptotically random codes perform better than projection.

title: A Lattice-Based MRF Model for Dynamic Near-Regular Texture Tracking
abstract: A near-regular texture (NRT) is a geometric and photometric deformation from its regular origin - a congruent wallpaper pattern formed by 2D translations of a single tile. A dynamic NRT is an NRT under motion. Although NRTs are pervasive in man-made and natural environments, effective computational algorithms for NRTs are few. This paper addresses specific computational challenges in modeling and tracking dynamic NRTs, including ambiguous correspondences, occlusions, and drastic illumination and appearance variations. We propose a lattice-based Markov-random-field (MRF) model for dynamic NRTs in a 3D spatiotemporal space. Our model consists of a global lattice structure that characterizes the topological constraint among multiple textons and an image observation model that handles local geometry and appearance variations. Based on the proposed MRF model, we develop a tracking algorithm that utilizes belief propagation and particle filtering to effectively handle the special challenges of the dynamic NRT tracking without any assumption on the motion types or lighting conditions. We provide quantitative evaluations of the proposed method against existing tracking algorithms and demonstrate its applications in video editing

title: Coloring of distance graphs with intervals as distance sets
abstract: Let D be a set of positive integers. The distance graph G(Z, D) with distance set D is the graph with vertex set Z in which two vertices x, y are adjacent if and only if |x - y| ∈ D. The fractional chromatic number, the chromatic number, and the circular chromatic number of G(Z, D) for various D have been extensively studied recently. In this paper, we investigate the fractional chromatic number, the chromatic number, and the circular chromatic number of the distance graphs with the distance sets of the form Dm, [k,k'] = {1, 2, ..., m} - {k, k + 1, ..., k'}, where m, k, and k' are natural numbers with m ≥ k' ≥ k. In particular, we completely determine the chromatic number of G(Z, Dm, [2,k']) for arbitrary m, and k'.

title: Reconfiguration Procedures for a Polymorphic and Partitionable Multiprocessor
abstract: This correspondence presents a collection of reconfiguration procedures for a multiprocessor which employs multistage interconnection networks. These procedures are used to dynamically partitipn the multiprocessor into many subsystems, and reconfigure them to form a variety commonly used topologies to match task graphs. By examining the switching capability of the interconnection network, design rules for avoiding connection conflicts are exploited. Then, on the basis of these rules, parallel procedures are designed. With the procedures, a subsystem can be reconfigured in the form of the desired topologies without interfering with other subsystems. In addition, the reconfiguration of a subsystem can be accomplished in constant time, independently of subsystem size.