Web image retrieval re-ranking with relevance model
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: Video Classification and Retrieval with the Informedia Digital Video Library System
abstract: This paper is organized in three parts. The first part details some of the lower level shot classification work, the second part describes the ‘manual’ retrieval systems while the last section details the interactive retrieval system for the Carnegie Mellon University TREC Video Retrieval Track runs. The description of the data can be found elsewhere in the proceedings of the 2002 TREC conference video track overview.

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: Assessing Effectiveness in Video Retrieval
abstract: 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: 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: Neighbourhood Unions and Edge-Pancyclicity

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: Speaker Adaptation of Fuzzy-Perceptron-Based Speech Recognition
abstract: In this paper, we propose a speech recognition algorithm which utilizes hidden Markov models (HMM) and Viterbi algorithm for segmenting the input speech sequence, such that the variable-dimensional speech signal is converted into a fixed-dimensional speech signal, called TN vector. We then use the fuzzy perceptron to generate hyperplanes which separate patterns of each class from the others. The proposed speech recognition algorithm is easy for speaker adaptation when the idea of "supporting pattern" is used. The supporting patterns are those patterns closest to the hyperplane. When a recognition error occurs, we include all the TN vectors of the input speech sequence with respect to the segmentations of all HMM models as the supporting patterns. The supporting patterns are then used by the fuzzy perceptron to tune the hyperplane that can cause correct recognition, and also tune the hyperplane that resulted in wrong recognition. Since only two hyperplane need to be tuned for a recognition error, the proposed adaptation scheme is time-economic and suitable for on-line adaptation. Although the adaptation scheme cannot ensure to correct the wrong recognition right after adaptation, the hyperplanes are tuned in the direction for correct recognition iteratively and the speed of adaptation can be adjusted by a "belief" parameter set by the user. Several examples are used to show the performance of the proposed speech recognition algorithm and the speaker adaptation scheme.

title: A Top-Down Processor Allocation Scheme for Hypercube Computers
abstract: An efficient processor allocation policy is presented for hypercube computers. The allocation policy is called free list since it maintains a list of free subcubes available in the system. An incoming request of dimension k (2/sup k/ nodes) is allocated by finding a free subcube of dimension k or by decomposing an available subcube of dimension greater than k. This free list policy uses a top-down allocation rule in contrast to the bottom-up approach used by the previous bit-map allocation algorithms. This allocation scheme is compared to the buddy, gray code (GC), and modified buddy allocation policies reported for the hypercubes. It is shown that the free list policy is optimal in a static environment, as are the other policies, and it also gives better subcube recognition ability compared to the previous schemes in a dynamic environment. The performance of this policy, in terms of parameters such as average delay, system utilization, and time complexity, is compared to the other schemes to demonstrate its effectiveness. The extension of the algorithm for parallel implementation, noncubic allocation, and inclusion/exclusion allocation is also given.

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