Automatic caption localization for photographs on World Wide Web pages

Rowe, Neil C.; Frew, Brian

doi:10.1016/s0306-4573(97)00048-4

Cited by 24 publications

(10 citation statements)

References 7 publications

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“…4,6 A sample of our training set showed that 95 percent of the photographs had captions, whereas 10 percent of the nonphotographs had captions. Both can be stored in similar image formats, so some content analysis is necessary to confirm photographs.…”

Section: Deciding Whether An Image Is a Photograph Or A Graphicmentioning

confidence: 99%

“…Frew and I previously reported a precision of 0.014 for text queries for a standard browser that tried to find pages with images matching particular words. 4 Recall is harder to estimate, but we got 0.97 in a manual inspection of 20 random Web pages (we defined recall as the fraction of the image-describing text on these pages that was found by our page scanner). The missed caption text was in paragraphs insufficiently related to corresponding images.…”

Section: The Web Crawler and Page Scannermentioning

confidence: 99%

“…[4][5][6] By looking for the right clues, large amounts of Web page text can be excluded as captions for any given image, and the captions in the remaining text can be inferred. Clues can include caption candidate wording, HTML constructs around the candidate, distance from the associated image, image-file name words, and associated image properties.…”

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confidence: 99%

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

Rowe

2002

IEEE Intell. Syst.

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Section: Deciding Whether An Image Is a Photograph Or A Graphicmentioning

confidence: 99%

Section: The Web Crawler and Page Scannermentioning

confidence: 99%

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

Rowe

2002

IEEE Intell. Syst.

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“…In text-based context search (B), the query method also consists of keywords, but the annotation is automatically derived from the context of the image within the document [6,8]. This approach may be brittle, because only in some domains (e.g., science, journalism) there exists something like a grammar for the juxtaposition of text and image which allows for finding meaningful correspondences between textual and pictorial content.…”

Section: Introductionmentioning

confidence: 99%

New use for the pen: outline-based image queries

Schomaker

Vuurpijl

Leau

1999

Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318)

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show abstract

“…For each of those images, the system calculates a score that represents the intensity of the relation between the image and the query keywords. Note that an evaluation method used here is based on simple HTML tag analysis, which is similar to a method commonly used in Web image search engines [10,15,16]. The score is calculated by checking the following conditions.…”

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confidence: 99%

Generic image classification using visual knowledge on the web

Yanai¹

2003

Proceedings of the Eleventh ACM International Conference on Multimedia - MULTIMEDIA '03

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In this paper, we describe a generic image classification system with an automatic knowledge acquisition mechanism from the World Wide Web. Due to the recent spread of digital imaging devices, the demand for image recognition of various kinds of real world scenes becomes greater. For realizing it, visual knowledge on various kinds of scenes is required. Then, we propose gathering visual knowledge on real world scenes for generic image classification from the World Wide Web. Our system gathers a large number of images from the Web automatically and makes use of them as training images for generic image classification. It consists of three modules, which are an image-gathering module, an image-learning module and an image classification module. The image-gathering module gathers images related to given class keywords from the Web automatically. The learning module extracts image features from gathered images and associates them with each class. The image classification module classifies an unknown image into one of the classes corresponding to the class keywords by using the association between image features and classes. In the experiments, the system demonstrated potential for generic image classification/recognition using images gathered from the World Wide Web automatically as training images.

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Automatic caption localization for photographs on World Wide Web pages

Cited by 24 publications

References 7 publications

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

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

New use for the pen: outline-based image queries

Generic image classification using visual knowledge on the web

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