Marie-4: a high-recall, self-improving Web crawler that finds images using captions

Rowe, Neil C.

doi:10.1109/mis.2002.1024745

Cited by 24 publications

(10 citation statements)

References 9 publications

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“…In 2002 Neil C. Rowe, proposed a intelligent agent Web crawler [20] and caption filter, searches the Web to find image captions and the associated image objects. He mainly searches the clues words from the captions of Meta data, and other text clues except captions.…”

Section: Related Workmentioning

confidence: 99%

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Online Information Search from Tamil Document Images in World Wide Web

Abirami¹,

Murugappan²

2012

IJCA

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Information Retrieval (IR) from Tamil document images present in World Wide Web (WWW) has become a challenging problem today due to its rising popularity. Among the most valuable Web assets, categorizing web images and retrieval of information from the images on the Web is quite difficult. This paper proposes a simple and effective method to separate the document images from the available web image sources and to retrieve the information present in those web document images. This system works in two phases: In the first phase, it concentrates on Automatic Image categorization process over web images by employing a filtering technique to discriminate the document images from other images available in WWW. Filtering technique employed here captures the image information by intensity and frequency histograms to discriminate the web document images. As for information retrieval in the second phase, feature string generation technique has been used to generate feature strings for every word images by extracting its shape this generates a feature string for every word image by extracting its features relying on their statistical properties, such as lines, black and white disposition rates and outline features of characters, instead of recognizing the letters and assigning its ASCII value like OCR. This kind of information retrieval has been initiated over a list of web sites and experimental results are recorded.

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Section: Related Workmentioning

confidence: 99%

“…He mainly searches the clues words from the captions of Meta data, and other text clues except captions. It uses a broad set of criteria to yield higher recall than competing systems, which generally focus on high precision [20].…”

Section: Related Workmentioning

confidence: 99%

Online Information Search from Tamil Document Images in World Wide Web

Abirami¹,

Murugappan²

2012

IJCA

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“…In many cases the media can be inferred to be decorative and can be eliminated, as for many banners and sidebars on pages as well as background sounds. Simple criteria can distinguish decorative graphics from photographs (Rowe, 2002): size (photographs are larger), frequency of the most common color (graphics have a higher frequency), number of different colors (photographs have more), extremeness of the colors (graphics are more likely to have pure colors), and average variation in color between adjacent pixels in the image (photographs have less). (Hu and Bagga, 2004) extends this to classify images in order of importance as "story", "preview", "host", "commercial", "icons and logos", "headings", and "formatting".…”

Section: Content Rating By Importancementioning

confidence: 99%

Critical Issues in Content Repurposing for Small Devices

Rowe

2009

Encyclopedia of Multimedia Technology and Networking, Second Edition

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Small handheld devices are increasingly popular, but it is difficult to display images, audio, and video on them as users would like. The limitations of such devices in display and processing for multimedia require significant planning to overcome. We discuss several ideas that are being studied, including panning and zooming, translation, substitution of links, and reformulation of data. Many of these require some ranking of data content, so we also discuss methods for that, as well as the process of redoing a data display. This topic is a highly active area of research, and many innovations will be appearing soon.

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“…(Rowe, 2002). However, these techniques currently are used primarily for information retrieval on the Web, rather than for Web mining.…”

Section: Future Directionsmentioning

confidence: 99%

Searching and mining the web for personalized and specialized information

Chau

2005

SIGIR Forum

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With the rapid growth of the Web, users are often faced with the problem of information overload and find it difficult to search for relevant and useful information on the Web. Besides general-purpose search engines, there exist some alternative approaches that can help users perform searches on the Web more effectively and efficiently. Personalized search agents and specialized search engines are two such approaches. The goal of this research is to investigate how machine learning and artificial intelligence techniques can be used to improve these approaches.

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Marie-4: a high-recall, self-improving Web crawler that finds images using captions

Cited by 24 publications

References 9 publications

Online Information Search from Tamil Document Images in World Wide Web

Online Information Search from Tamil Document Images in World Wide Web

Critical Issues in Content Repurposing for Small Devices

Searching and mining the web for personalized and specialized information

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