Modeling timing features in broadcast news video classification
Broadcast news programs are well-structured video, and timing can be a strong predictor for specific types of news reports. However, learning a classifier using timing features may not be an easy task when training data are noisy. We approach the problem from the generative model perspective, and approximate the class density in a non-parametric fashion. The results show that timing is a simple but extremely effective feature, and our method can achieve significantly better performance than a discriminative classifier.

title: Meta-Classification of Multimedia Classifiers
abstract: Combining multiple classifiers is of particular interest in the multimedia systems, since there is usually data of very different types/modalities that should be mined or analyzed. Our wearable ‘experience collection’ system unobtrusively records the wearer’s conversation, recognizes the face of the dialog partner and remembers his/her voice. When the system sees the same person’s face or hears the same voice it can then use a summary of the last conversation with this person to remind the wearer. To correctly identify a person from a mixture of video and audio stream, classification judgments from individual modality classifiers must be combined effectively to yield a more accurate decision. To address the problems of combination strategy in previous studies, a meta-classification strategy using Support Vector Machine is proposed. Preliminary results show that combining different face recognition and speaker identification technology by meta-classification is dramatically more effective than weighted interpolation. Meta-classification is general enough to be applied to any application that needs to combine multiple classifiers without much modification.

title: Extreme video retrieval: joint maximization of human and computer performance
abstract: We present an efficient system for video search that maximizes the use of human bandwidth, while at the same time exploiting the machine's ability to learn in real-time from user selected relevant video clips. The system exploits the human capability for rapidly scanning imagery augmenting it with an active learning loop, which attempts to always present the most relevant material based on the current information. Two versions of the human interface were evaluated, one with variable page sizes and manual paging, the other with a fixed page size and automatic paging. Both require absolute attention and focus of the user for optimal performance. In either case, as users search and find relevant results, the system can invisibly re-rank its previous best guesses using a number of knowledge sources, such as image similarity, text similarity, and temporal proximity. Experimental evidence shows a significant improvement using the combined extremes of human and machine power over either approach alone.

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: News video classification using SVM-based multimodal classifiers and combination strategies
abstract: 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: 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: 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.

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: Circular Chromatic Numbers and Fractional Chromatic Numbers of Distance Graphs with Distance Sets Missing An Interval

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: Several parameters of generalized Mycielskians
abstract: The generalized Mycielskians (also known as cones over graphs) are the natural generalization of the Mycielski graphs (which were first introduced by Mycielski in 1955). Given a graph G and any integer m ≥ 0, one can transform G into a new graph µm (G), the generalized Mycielskian of G. This paper investigates circular clique number, total domination number, open packing number, fractional open packing number, vertex cover number, determinant, spectrum, and biclique partition number of µm (G).