Intelligent Search for Image Information on the Web through Text and Link Structure Analysis

Abstract

Searching for effective methods to retrieve information from the World Wide Web (WWW) has been in the center of many research efforts during the last few years. The relevant technology evolved rapidly thanks to advances in Web systems technology [1] and information retrieval research [15]. Image retrieval on the Web, in particular, is a very important problem in itself [8].The relevant technology has also evolved significantly propelled by advances in image database research [20].Several approaches to the problem of content-based image retrieval on the Web have been proposed and some have been implemented on research prototypes (e.g., ImageRover [23],WebSEEK [21]) and commercial systems. The last category of systems, includes general purpose image search engines (e.g., Google Image Search 1 , Yahoo 2 , Altavista 3 ) as well as systems providing specific services to users such as detection of unauthorized use of images, Web and e-mail content filters, image authentication, licensing and advertising.Image retrieval on the Web requires that content descriptions be extracted from Web pages and used to determine which Web pages contain images that satisfy the query selection criteria. The methods and systems referred to above differ in the type of content descriptions used and in the search methods applied. There are four main approaches to Web image search and retrieval.Retrieval by text content: Typically images on the Web are described by text or attributes associated with images in html tags (e.g., filename, caption, alternate text etc.). These are automatically extracted from the Web pages and are used in retrievals. Google, Yahoo, and AltaVista are example systems of this category. The importance of the various text fields in retrieving images by text content depends also on their relative location with regard to the location of the images within the Web pages [19].

Link-Driven Study to Enhance Text-Based Image Retrieval: Implicit Links Versus Explicit Links

“…However, PicASHOW supports only keyword queries and cannot handle image content or example image queries. To solve this problem, [43] and [44] proposed WPicASHOW (weighted PicASHOW), allowing a weighted ranking of cocitation analysis that is based on the combination of textual and visual content to regulate the influence of links between pages. The experiments showed a better performance of WPicASHOW over PicASHOW.…”

Combination of document structure and links for multimedia object retrieval

“…In the PicASHOW system, only textual queries are supported. For this reason, Voutsakis et al [20] and Petrakis [21] enhanced this system by incorporating the text and image content of the query and the page. The new system, called WPicASHOW (Weighted PicASHOW), has given improved results.…”