1168192
Approximately Repetitive Structure Detection for Wrapper Induction
In recent years, much work has been invested into automatically learning wrappers for information extraction from HTML tables and lists. Our research has focused on a system that can learn a wrapper from a single unlabelled page. An essential step is to locate the tabular data within the page. This is not trivial when the structures of data tuples are similar but not identical. In this paper we describe an algorithm that can automatically detect approximate repetitive structures within one sequence. The algorithm does not rely on any domain knowledge or HTML heuristics and it can be used in detecting repetitive patterns and hence to learn wrappers from a single unlabeled tabular page.
1036423
Investigation of Brood Size in GP with Brood Recombination Crossover for Object Recognition
This paper describes an approach to the investigation of brood size in the brood recombination crossover method in genetic programming for object recognition problems. The approach is examined and compared with the standard crossover operator on three object classification problems of increasing difficulty. The results suggest that the brood recombination method outperforms the standard crossover operator for all the problems in terms of the classification accuracy. As the brood size increases, the system effective performance can be improved. When it exceeds a certain point, however, the effective performance will not be improved and the system will become less efficient.
7186941
A Cartoon Video Detection Method Based on Active Relevance Feedback and SVM
By analyzing the particular features of visual content for cartoon videos, 8 typical features of MPEG-7 descriptors are extracted to distinguish the cartoons from other videos. Then, a content-based video classifier is developed by combining the active relevance feedback technique and SVM for detecting the cartoon videos. The experimental results on the vast real video clips illustrate that compared with the classifier based on SVM and that based on traditional relevance feedback technique and SVM, the proposed classifier has a higher advantage of cartoon video detection.
13928844
Unsupervised video-shot segmentation and model-free anchorperson detection for news video story parsing
News story parsing is an important and challenging task in a news video library system. We address two important components in a news video story parsing system: shot boundary detection and anchorperson detection. First, an unsupervised fuzzy c-means algorithm is used to detect video-shot boundaries in order to segment a news video into video shots. Then, a graph-theoretical cluster analysis algorithm is implemented to classify the video shots into anchorperson shots and news footage shots. Because of its unsupervised nature, the algorithms require little human intervention. The efficacy of the proposed method is extensively tested on more than five hours of news programs.
206603960
FCM-Based Clustering Algorithm Ensemble for Large Data Sets
In the field of cluster analysis, most of the available algorithms were designed for small data sets, which cannot efficiently deal with large scale data set encountered in data mining. However, some sampling-based clustering algorithms for large scale data set cannot achieve ideal result. For this purpose, a FCM-based clustering ensemble algorithm is proposed. Firstly, it performs the atom clustering algorithm on the large data set. Then, randomly select a sample from each atom as representative to reduce the data amount. And the ensemble learning technique is used to improve the clustering performance. For the complex large data sets, the new algorithm has high classification speed and robustness. The experimental results illustrate the effectiveness of the proposed clustering algorithm.
2376280
A Radar Target Multi-feature Fusion Classifier Based on Rough Neural Network
Fusing Multi-feature will benefit radar target classification with much more belief. However, since radar target attributes such as high range resolution profiles, waveforms, frequency spectra, time-frequency spectra, higher order statistics, polarization spectra and flight path are of different dimensions, it is hard to make decision by fusing multi-feature directly. Fortunately, rough set makes decision by examining the fitness of each condition attribute separately, while neural network is powerful for dealing with nonlinear problems. With radial projection of target dimension, cruising velocity and height as condition attribute, a multi-feature rough neural network fusion classifier is presented. Simulation of the proposed classifier based on an information system with 25 targets belonging to 6 classes shows accuracy not less than 93%, while attributes are subjoined with typical radar errors.
33275321
A Shot Boundary Detection Method for News Video Based on Rough Sets and Fuzzy Clustering
With the rapid growing amount of multimedia, content-based infomation retrieval has become more and more important. As a crucial step in content-based news video indexing and retrieval system, shot boundary detection attracts much more research interests in recent years. To partition news video into shots, many metrics were constructed to measure the similarity among video frames based on all the available video features. However, too many features will reduce the efficiency of the shot boundary detection. Therefore, it is necessary to perform feature reduction for every decision of shot boundary. For this purpose, the classification method based on rough sets and fuzzy c-means clustering for feature reduction and rule generation is proposed. According to the particularity of news scenes, shot transition can be divided into three types: cut transition, gradual transition and no transition. The efficacy of the proposed method is extensively tested on more than 2 h of news programs and 98.0% recall with 96.6% precision have been achieved.