Fast text categorization using concise semantic analysis

Text representation is a necessary procedure for text categorization tasks. Currently, bag of words (BOW) is the most widely used text representation method but it suffers from two drawbacks. First, the quantity of words is huge; second, it is not feasible to calculate the relationship between words. Semantic analysis (SA) techniques help BOW overcome these two drawbacks by interpreting words and documents in a space of concepts. However, existing SA techniques are not designed for text categorization and often incur huge computing cost. This paper proposes a concise semantic analysis (CSA) technique for text categorization tasks. CSA extracts a few concepts from category labels and then implements concise interpretation on words and documents. These concepts are small in quantity and great in generality and tightly related to the category labels. Therefore, CSA preserves necessary information for classifiers with very low computing cost. To evaluate CSA, experiments on three data sets (Reuters-21578, 20-NewsGroup and Tancorp) were conducted and the results show that CSA reaches a comparable micro- and macro-F₁ performance with BOW, if not better one. Experiments also show that CSA helps dimension sensitive learning algorithms such as k-nearest neighbor (kNN) to eliminate the “Curse of Dimensionality” and as a result reaches a comparable performance with support vector machine (SVM) in text categorization applications. In addition, CSA is language independent and performs equally well both in Chinese and English.     

An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows

Binary image representation is essential format for document analysis. In general, different available binarization techniques are implemented for different types of binarization problems. The majority of binarization techniques are complex and are compounded from filters and existing operations. However, the few simple thresholding methods available cannot be applied to many binarization problems. In this paper, we propose a local binarization method based on a simple, novel thresholding method with dynamic and flexible windows. The proposed method is tested on selected samples called the DIBCO 2009 benchmark dataset using specialized evaluation techniques for binarization processes. To evaluate the performance of our proposed method, we compared it with the Niblack, Sauvola and NICK methods. The results of the experiments show that the proposed method adapts well to all types of binarization challenges, can deal with higher numbers of binarization problems and boosts the overall performance of the binarization.     

Document clustering using synthetic cluster prototypes

The use of centroids as prototypes for clustering text documents with the k-means family of methods is not always the best choice for representing text clusters due to the high dimensionality, sparsity, and low quality of text data. Especially for the cases where we seek clusters with small number of objects, the use of centroids may lead to poor solutions near the bad initial conditions. To overcome this problem, we propose the idea of synthetic cluster prototype that is computed by first selecting a subset of cluster objects (instances), then computing the representative of these objects and finally selecting important features. In this spirit, we introduce the MedoidKNN synthetic prototype that favors the representation of the dominant class in a cluster. These synthetic cluster prototypes are incorporated into the generic spherical k-means procedure leading to a robust clustering method called k-synthetic prototypes (k-sp). Comparative experimental evaluation demonstrates the robustness of the approach especially for small datasets and clusters overlapping in many dimensions and its superior performance against traditional and subspace clustering methods.     

Improvements in image categorization using codebook ensembles

The problem of object category classification by committees or ensembles of classifiers, each of which is based on one diverse codebook, is addressed in this paper. Two methods of constructing visual codebook ensembles are proposed in this study. The first technique introduces diverse individual visual codebooks using different clustering algorithms. The second uses various visual codebooks of different sizes for constructing an ensemble with high diversity. Codebook ensembles are trained to capture and convey image properties from different aspects. Based on these codebook ensembles, different types of image representations can be acquired. A classifier ensemble can be trained based on different expression datasets from the same training image set. The use of a classifier ensemble to categorize new images can lead to improved performance. Detailed experimental analysis on a Pascal VOC challenge dataset reveals that the present ensemble approach performs well, consistently improves the performance of visual object classifiers, and results in state-of-the-art performance in categorization.     

一种用于提高字符识别速度的字符预分类法研究

文章提出了一种用于提高字符识别速度的字符预分类法,给出了一个带有容差分析的文本行字符基线精确测定算法,模糊逻辑用于确定各字符类的隶属值以保证字符的正确归类。实验结果表明该方法有满意的处理结果。     

多媒体文档管理系统的设计与实现

该文介绍了多媒体文档管理系统的一种设计方法,并针对几种常用格式文档的处理技术进行了探讨。     

A noise attribute thresholding method for document image binarization

A new thresholding method, called the noise attribute thresholding method (NAT), for document image binarization is presented in this paper. This method utilizes the noise attribute features extracted from the images to make the selection of threshold values for image thresholding. These features are based on the properties of noise in the images and are independent of the strength of the signals (objects and background) in the image. A simple noise model is given to explain these noise properties. The NAT method has been applied to the problem of removing text and figures printed on the back of the paper. Conventional global thresholding methods cannot solve this kind of problem satisfactorily. Experimental results show that the NAT method is very effective. Received July 05, 1999 / Revised July 07, 2000     

一种优化的网络故障查找模型

陈述了故障链的概念、二分查找隔离方法,介绍了一种优化的计算机网络故障查找模型,利用该模型可以迅速准确地诊断和排除网络故障。     

独立于语种的文本分类方法

文本分类是指在给定分类体系下,根据文本的内容自动确定文本类别的过程。本文提出了一个基于机器学习的、独立于语种的文本分类模型,并对模型中的特征抽取、分类器和评价方法进行了详细的介绍。该模型已经在中文和日文两个语种的新闻语料上得到实现,并获得了较好的分类性能。     

基于工作流技术的机关办公业务模拟教学系统的分析和设计

机关办公业务系统是电子政务系统的重要组成部分。本文介绍了工作流的基本概念,利用工作流为理论基础和政府机关公文流转实例建立公文流转模型,采用．NET语言和SQL Server数据库技术来实现机关办公业务模拟教学系统。