Extracting Content Structure for Web Pages Based on Visual Representation

Cai, Deng; Yu, Shipeng; Wen, Ji-Rong; Ma, Wei‐Ying

doi:10.1007/3-540-36901-5_42

Cited by 249 publications

(166 citation statements)

References 16 publications

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“…Vision-based Page Segmentation, or VIPS [8] for short, is intended to find all of the regions of which a document is composed. It builds on the hypothesis that web designers provide visual cues that help people recognize the different regions of which a document is composed, e.g., horizontal or vertical rules, boxes, colored panels, special fonts, or background images.…”

Section: Vips [8]mentioning

confidence: 99%

I-ViDE: An Improved Vision-Based Approach for Deep Web Data Extraction

Varade¹,

Jethani²

2014

IOSRJCE

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Section: Vips [8]mentioning

confidence: 99%

I-ViDE: An Improved Vision-Based Approach for Deep Web Data Extraction

Varade¹,

Jethani²

2014

IOSRJCE

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“…VIPS algorithm is a popular algorithm for web page segmentation based on the vision of a human and hence useful in many contexts like information extraction, information retrieval and automatic understanding of a web page (Cai, Yu, Wen, & Ma, 2003). VIPS algorithm makes use of the HTML DOM (Document Object Model) tree and extracts suitable blocks from it.…”

Section: Snippet Extraction Using Vips Algorithmmentioning

confidence: 99%

A Scalable Image Snippet Extraction Framework for Integration with Search Engines

Sarwar

Rahman

Ali

et al. 2013

CIS

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Search result visualization is a task performed by search engines that enables users to find their desired documents, in an effective and efficient manner. Image based summary or best images of a web document, displayed as a part of the visualization process, has become indispensable, as a human perceives images instantaneously. But, selection of the best image increases latency in search result generation, and workload for the search process. In this paper, we propose and implement a search framework by integrating text and image search engines that increases the speed of extracting a representative image of a web document. Text associated with an image, image area and position are incorporated with the ranking function that finds the image snippet. By comparison, we show that our framework significantly improves over the existing ones in terms of time complexity, while maintaining the quality of image based summaries.

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“…Users discover a lot of loaded hyper structure as the growth on the web is massive [4], [9], [13]. Updating incoming data and extracting relevant information without redundancy from the web quickly and efficiently becomes a growing concern among web mining research communities [6], [8].…”

Section: Introductionmentioning

confidence: 99%

Noise Reduction and Content Retrieval from Web Pages

Lingwal¹

2013

IJCA

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The World Wide Web is the most increasingly growing and accessible source of information. Web contents of different fields which can offer important information to users are available in the Web like multimedia data, structured, semistructured and unstructured data. But only a part of the information is useful for a particular application and the remaining information are considered as noises. Data on web pages contain formatting code, advertisement, navigation links, etc. This collection of unwanted noise with the real content in a web page complicates the task of automatic information extraction and processing. This requires the extraction of useful noise-free information. Otherwise, it can ruin the effectiveness of Web mining techniques. This paper proposes a novel method to filter web pages and retrieve the actual content of a web page. This research work proposed an approach for removing the noises from a given web page which will improve the performance of web content mining. At first, the web page information is divided into various blocks which then tokenized to separate the informative content from noises. This paper presents algorithm for removing noises from the web page and automatically extract important web content. This paper also presents the algorithm for global noise removal.

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Extracting Content Structure for Web Pages Based on Visual Representation

Cited by 249 publications

References 16 publications

I-ViDE: An Improved Vision-Based Approach for Deep Web Data Extraction

I-ViDE: An Improved Vision-Based Approach for Deep Web Data Extraction

A Scalable Image Snippet Extraction Framework for Integration with Search Engines

Noise Reduction and Content Retrieval from Web Pages

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