A Domain Based Approach to Crawl the Hidden Web

There is a lot of research work being performed on indexing the Web. More and more sophisticated Web crawlers are been designed to search and index the Web faster. But all these traditional crawlers crawl only the part of Web we call “Surface Web”. They are unable to crawl the hidden portion of the Web. These traditional crawlers retrieve contents only from surface Web pages which are just a set of Web pages linked by some hyperlinks and ignoring the hidden information. Hence, they ignore tremendous amount of information hidden behind these search forms in Web pages. Most of the published research has been done to detect such searchable forms and make a systematic search over these forms. Our approach here will be based on a Web crawler that analyzes search forms and fills tem with appropriate content to retrieve maximum relevant information from the database

A Distributed Approach to Crawl Domain Specific Hidden Web

A large amount of on-line information resides on the invisible web - web pages generated dynamically from databases and other data sources hidden from current crawlers which retrieve content only from the publicly indexable Web. Specially, they ignore the tremendous amount of high quality content hidden behind search forms, and pages that require authorization or prior registration in large searchable electronic databases. To extracting data from the hidden web, it is necessary to find the search forms and fill them with appropriate information to retrieve maximum relevant information. To fulfill the complex challenges that arise when attempting to search hidden web i.e. lots of analysis of search forms as well as retrieved information also, it becomes eminent to design and implement a distributed web crawler that runs on a network of workstations to extract data from hidden web. We describe the software architecture of the distributed and scalable system and also present a number of novel techniques that went into its design and implementation to extract maximum relevant data from hidden web for achieving high performance

Understanding User Intentions in Vertical Image Search

With the development of Internet and Web 2.0, large volume of multimedia contents have been made online. It is highly desired to provide easy accessibility to such contents, i.e. efficient and precise retrieval of images that satisfies users' needs. Towards this goal, content-based image retrieval (CBIR) has been intensively studied in the research community, while text-based search is better adopted in the industry. Both approaches have inherent disadvantages and limitations. Therefore, unlike the great success of text search, Web image search engines are still premature. In this thesis, we present iLike, a vertical image search engine which integrates both textual and visual features to improve retrieval performance. We bridge the semantic gap by capturing the meaning of each text term in the visual feature space, and re-weight visual features according to their significance to the query terms. We also bridge the user intention gap since we are able to infer the "visual meanings" behind the textual queries. Last but not least, we provide a visual thesaurus, which is generated from the statistical similarity between the visual space representation of textual terms. Experimental results show that our approach improves both precision and recall, compared with content-based or text-based image retrieval techniques. More importantly, search results from iLike are more consistent with users' perception of the query terms

A World Wide Web Region-Based Image Search Engine

content-based search engine for the World Wide Web is presented. Information Web Crawlers continuously traverse the Internet and collect images that are subsequently indexed based on integrated feature vectors. As a basis for the indexing, a novel K-Means segmentation algorithm is used, modified so as to take into account the coherence of individual regions. Based on the extracted regions, characteristic features are estimated using color, texture and shape/region boundary information. These features along with additional information are stored in a database. The user can access and search this indexed content through the Web with an advanced interface. Experimental results demonstrate the performance of the system, which can be reached in a publicly accessible web site