Assessing Effectiveness in Video Retrieval
This paper examines results from the last two years of the TRECVID video retrieval evaluations. While there is encouraging evidence about progress in video retrieval, there are several major disappointments confirming that the field of video retrieval is still in its infancy. Many publications blithely attribute improvements in retrieval tasks to the different techniques without paying much attention to the statistical reliability of the comparisons. We conduct an analysis of the official TRECVID evaluation results, using both retrieval experiment error rates and ANOVA measures, and demonstrate that the difference between many systems is not statistically significant. We conclude the paper with the lessons learned from both results with and without statistically significant difference.

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: 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: 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: Exploring the Synergy of Humans and Machines in Extreme Video Retrieval
abstract: We introduce an interface for efficient video search that exploits the human ability to quickly scan visual content, after automatic retrieval has arrange the images in expected order of relevance. While extreme video retrieval is taxing to the human, it is also extremely effective. Two variants of extreme retrieval are demonstrated, 1) RSVP which automatically pages through images with user-control of the paging speed, while the user marks relevant shots and 2) MBRP where the user manually controls paging and adjusts the number of images per page, depending on the density of relevant shots.

title: Circular chromatic numbers of some distance graphs
abstract: Given a set D of positive integers, the distance graph G(Z,D) has vertices all integers Z, and two vertices j and j^' in Z are adjacent if and only if |j-j^'|@?D. This paper determines the circular chromatic numbers of some distance graphs.

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

title: Analysis of two restart algorithms
abstract: Since the backpropagation algorithm used for neural network training suffers from a slow convergence and often sticking in local minima, the restart mechanism has been introduced, whose strategy is to cut off the training process and restart it with a fresh initialization when it seems unlikely to converge in a relatively short time. In this paper, we give detailed mathematical analysis on two versions of the restart algorithms. By deriving analytic expressions of the expected convergence time and the success rate, we illustrate why the restart algorithms work well and gain insights into the proper use of restarting. Numerical simulations are performed on the XOR problem, symmetry detection, parity problem and Arabic numeral recognition. We show the effectiveness of the restart algorithms, and compare them with simulated annealing. The analysis can also be applied to many other fields.

title: Some star extremal circulant graphs
abstract: The circular chromatic number @g"c(G) and the fractional chromatic number @g"f(G) are two generalizations of the ordinary chromatic number of a graph G. A graph is called star extremal if its circular chromatic number equals its fractional chromatic number. Gao and Zhu ( (1996) 147-156), Lih et al. ( (1999) 491-499) gave many classes of circulant graphs which are star extremal. In this paper, we study the star extremality of circulant graphs whose generating sets are of the form {1,2,...,m-1,k,k+1,...,k+m-2}, {k,k+1,...,k'}, and {k,k+1,...,k"1,k"2,k"2+1,...,@?p/[email protected]?}, where p is the vertex number of the graph. As a corollary, we give an improvement of a result of Gao and Zhu ( (1996) 147-156).

title: A Space-Time Delay Neural Network for Motion Recognition and its Application to Lipreading
abstract: Motion recognition has received increasing attention in recent years owing to heightened demand for computer vision in many domains, including the surveillance system, multimodal human computer interface, and traffic control system. Most conventional approaches classify the motion recognition task into partial feature extraction and time-domain recognition subtasks. However, the information of motion resides in the space-time domain instead of the time domain or space domain independently, implying that fusing the feature extraction and classification in the space and time domains into a single framework is preferred. Based on this notion, this work presents a novel Space-Time Delay Neural Network (STDNN) capable of handling the space-time dynamic information for motion recognition. The STDNN is unified structure, in which the low-level spatiotemporal feature extraction and high-level space-time-domain recognition are fused. The proposed network possesses the spatiotemporal shift-invariant recognition ability that is inherited from the time delay neural network (TDNN) and space displacement neural network (SDNN), where TDNN and SDNN are good at temporal and spatial shift-invariant recognition, respectively. In contrast to multilayer perceptron (MLP), TDNN, and SDNN, STDNN is constructed by vector-type nodes and matrix-type links such that the spatiotemporal information can be accurately represented in a neural network. Also evaluated herein is the performance of the proposed STDNN via two experiments. The moving Arabic numerals (MAN) experiment simulates the object's free movement in the space-time domain on image sequences. According to these results, STDNN possesses a good generalization ability with respect to the spatiotemporal shift-invariant recognition. In the lipreading experiment, STDNN recognizes the lip motions based on the inputs of real image sequences. This observation confirms that STDNN yields a better performance than the existing TDNN-based system, particularly in terms of the generalization ability. In addition to the lipreading application, the STDNN can be applied to other problems since no domain-dependent knowledge is used in the experiment.