Databases and Information Systems Commons^™

Capacity Constrained Assignment In Spatial Databases, Hou U Leong, Man Lung Yiu, Kyriakos Mouratidis, Nikos Mamoulis

Research Collection School Of Computing and Information Systems

Given a point set P of customers (e.g., WiFi receivers) and a point set Q of service providers (e.g., wireless access points), where each q 2 Q has a capacity q.k, the capacity constrained assignment (CCA) is a matching M Q × P such that (i) each point q 2 Q (p 2 P) appears at most k times (at most nce) in M, (ii) the size of M is maximized (i.e., it comprises min{|P|,P q2Q q.k} pairs), and (iii) the total assignment cost (i.e., the sum of Euclidean distances within all pairs) is minimized. Thus, the CCA problem is …

A Multi-Scale Tikhonov Regularization Scheme For Implicit Surface Modeling, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Kernel machines have recently been considered as a promising solution for implicit surface modelling. A key challenge of machine learning solutions is how to fit implicit shape models from large-scale sets of point cloud samples efficiently. In this paper, we propose a fast solution for approximating implicit surfaces based on a multi-scale Tikhonov regularization scheme. The optimization of our scheme is formulated into a sparse linear equation system, which can be efficiently solved by factorization methods. Different from traditional approaches, our scheme does not employ auxiliary off-surface points, which not only saves the computational cost but also avoids the problem …

Extraction Of Coherent Relevant Passages Using Hidden Markov Models, Jing Jiang, Chengxiang Zhai

Research Collection School Of Computing and Information Systems

In information retrieval, retrieving relevant passages, as opposed to whole documents, not only directly benefits the end user by filtering out the irrelevant information within a long relevant document, but also improves retrieval accuracy in general. A critical problem in passage retrieval is to extract coherent relevant passages accurately from a document, which we refer to as passage extraction. While much work has been done on passage retrieval, the passage extraction problem has not been seriously studied. Most existing work tends to rely on presegmenting documents into fixed-length passages which are unlikely optimal because the length of a relevant passage …

Hot Event Detection And Summarization By Graph Modeling And Matching, Yuxin Peng, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

This paper proposes a new approach for hot event detection and summarization of news videos. The approach is mainly based on two graph algorithms: optimal matching (OM) and normalized cut (NC). Initially, OM is employed to measure the visual similarity between all pairs of events under the one-to-one mapping constraint among video shots. Then, news events are represented as a complete weighted graph and NC is carried out to globally and optimally partition the graph into event clusters. Finally, based on the cluster size and globality of events, hot events can be automatically detected and selected as the summaries of …

A Review Of Data Mining Techniques, Sang Jun Lee, Keng Siau

Research Collection School Of Computing and Information Systems

Terabytes of data are generated everyday in many organizations. To extract hidden predictive information from large volumes of data, data mining (DM) techniques are needed. Organizations are starting to realize the importance of data mining in their strategic planning and successful application of DM techniques can be an enormous payoff for the organizations. This paper discusses the requirements and challenges of DM, and describes major DM techniques such as statistics, artificial intelligence, decision tree approach, genetic algorithm, and visualization.

Design And Implementation Of Two Text Recognition Algorithms, Madhumathi Yendamuri

Theses

This report presents two algorithms for text recognition. One is a neural-based orthogonal vector with pseudo-inverse approach for pattern recognition. A method to generate N orthogonal vectors for an N-neuron network is also presented. This approach converges the input to the corresponding orthogonal vector representing the prototype vector. This approach can restore an image to the original image and thus has error recovery capacility. Also, the concept of sub-networking is applied to this approach to enhance the memory capacity of the neural network. This concept drastically increases the memory capacity of the network and also causes a reduction of the …

New Algorithms For Mid-Crack Codes In Image Processing, Wai-Tak Wong

Theses

The chain code is a widely-used description for a contour image. Recently, a mid-crack code algorithm has been proposed as another more precise method for image representation. New algorithms using this new mid-crack code for image representation, restoration, and skeletonization are developed. The efficiency and accuracy can be increased obviously.

Firstly, the conversion of a binary image with multiple regions into the mid-crack codes is presented. A fast on-line implementation can be achieved using tables look-up. The input binary image may contain several object regions and their mid-crack codes can be extracted at the same time in a single-pass row-by-row …