40362881
A Knowledge Learning Approach to Information Extraction from Multiple Text Based Web Sites
This paper describes a learning/adaptive approach to automatically building knowledge bases for information extraction from text based web pages. A frame based representation is introduced to represent domain knowledge as knowledge unit frames. A frame learning algorithm is developed to automatically learn knowledge unit frames from training examples. Some training examples can be obtained by automatically parsing a number of tabular web pages in the same domain, which greatly reduced the amount of time consuming manual work. This approach was investigated on ten web sites of real estate advertisements and car advertisements and nearly all the information was successfully extracted with very few false alarms. These results suggest that both the knowledge unit frame representation and the frame learning algorithm work well, domain specific knowledge bases can be learned from training examples, and the domain specific knowledge base can be used for information extraction from flexible text-based semi-structured Web pages on multiple Web sites. The investigation of the knowledge representation on five other domains suggests that this approach can be easily applied to other domains by simply changing the training examples.
43193547
Knowledge-Based Information Agents
This paper explains our approach to building knowledge-based information agents. Instead of building an agent from scratch, we advocate building an agent by adding knowledge to a framework. Knowledge is classified into three categories - general knowledge, domain specific knowledge and site specific knowledge - which enables knowledge reuse and sharing. The paper details the agent architecture, the components of each category of knowledge and the main functions of the framework.
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.
32198408
Modelling Citation Networks for Improving Scientific Paper Classification Performance
This paper describes an approach to the use of citation links to improve the scientific paper classification performance. In this approach, we develop two refinement functions, a linear label refinement (LLR) and a probabilistic label refinement (PLR), to model the citation link structures of the scientific papers for refining the class labels of the documents obtained by the content-based Naive Bayes classification method. The approach with the two new refinement models is examined and compared with the content-based Naive Bayes method on a standard paper classification data set with increasing training set sizes. The results suggest that both refinement models can significantly improve the system performance over the content-based method for all the training set sizes and that PLR is better than LLR when the training examples are sufficient.
10164763
Automatic Pattern Construction For Web Information Extraction
This paper describes a domain independent approach for automatically constructing information extraction patterns for semi-structured web pages. Given a randomly chosen page from a web site of similarly structured pages, the system identifies a region of the page that has a regular "tabular" structure, and then infers an extraction pattern that will match the "rows" of the region and identify the data elements. The approach was tested on three corpora containing a series of tabular web sites from different domains and achieved a success rate of at least 80%. A significant strength of the system is that it can infer extraction patterns from a single training page and does not require any manual labeling of the training 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.
36004220
A Video Shot Boundary Detection Algorithm Based on Feature Tracking
Partitioning a video sequence into shots is the first and key step toward video-content analysis and content-based video browsing and retrieval. A novel video shot boundary detection algorithm is presented based on the feature tracking. First, the proposed algorithm extracts a set of corner-points as features from the first frame of a shot. Then, based on the Kalman filtering, these features are tracked with windows matching method from the subsequent frames. According to the characteristic pattern of pixels intensity changing between corresponding windows, the measure of shot boundary detection can be obtained to confirm the types of transitions and the time interval of gradual transitions. The experimental results illustrate that the proposed algorithm is effective and robust with low computational complexity
1210426
De-interlacing Algorithm Based on Motion Objects
A novel de-interlacing algorithm based on motion objects is presented in this paper. In this algorithm, natural motion objects, not contrived blocks, are considered as the processing cells, which are accurately detected by a new scheme, and whose matching objects are quickly searched by the immune clonal selection algorithm. This novel algorithm integrates many other de-interlacing methods, so it is more adaptive to various complex video sequences. Moreover, it can perform the motion compensation for objects with the translation, rotation as well as the scaling transform. The experimental results illustrate that compared with the block matching method with full search, the proposed algorithm greatly improve the efficiency and performance.
28211985
A Fully Unsupervised Image Segmentation Algorithm Based on Wavelet-Domain Hidden Markov Tree Models
A fully unsupervised image segmentation algorithm is presented in this paper, in which wavelet-domain hidden Markov tree (WD-HMT) model is exploited together with the cluster analysis and validity techniques. The true number of textures in a given image is determined by calculating the likelihood disparity of textures using the modified partition fuzzy degree (MPFD) function at one suitable scale. Then, possibilistic C-means (PCM) clustering is performed to determine the training sample data from different textures according to the true number of textures obtained. The unsupervised segmentation is changed into self-supervised one, and the HMTseg algorithm is used to achieve the final segmentation results. This algorithm is applied to segment a variety of composite texture images into distinct homogeneous regions and good segmentation results are reported.
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.