Unifying Textual and Visual Cues for Content-Based Image Retrieval on the World Wide Web

Sclaroff, Stan; Cascia, Marco La; Sethi, Sunjay; Taycher, Leonid

doi:10.1006/cviu.1999.0765

Cited by 109 publications

(95 citation statements)

References 39 publications

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“…Some WWW image search systems such as WebSeer [3], WebSEEk [4] and Image Rover [5] have been reported so far, which can be regarded as an integration of keyword-based search and content-based image retrieval. These systems search for images based on the query keywords, and then a user selects query images from search results.…”

Section: Our Methods Of Image-gatheringmentioning

confidence: 99%

A Fast Image-Gathering System on the World-Wide Web Using a PC Cluster

Yanai

Shindo

Noshita

2001

Web Intelligence: Research and Development

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Abstract. Thanks to the recent explosive progress of WWW (WorldWide Web), we can easily access a large number of images from WWW. There are, however, no established methods to make use of WWW as a large image database. In this paper, we describe an automatic imagegathering system from WWW, in which we use both keywords and image features. By exploiting some existing keyword-based search engines and selecting images by their image features, our system obtains, with high accuracy, images that are strongly related to query keywords. This system has been implemented on a parallel PC cluster, which enables us to gather more than one hundred images from WWW in about one minute.

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Section: Our Methods Of Image-gatheringmentioning

confidence: 99%

A Fast Image-Gathering System on the World-Wide Web Using a PC Cluster

Yanai

Shindo

Noshita

2001

Web Intelligence: Research and Development

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“…Since the results have been encouraging [37,23,54], they motivated the research of more advanced combinations such as dynamically weighted features [68].…”

Section: Multi-modal Informationmentioning

confidence: 99%

Adaptive relevance feedback for large-scale image retrieval

Suditu

Fleuret

2015

Multimed Tools Appl

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“…The VOP, depending on the corresponding VO from which has been extracted, is in general the object of interest and can be characterized in terms of its visual features in an automatic way. In particular, based on our experience in the field of image and video retrieval [12], [5], we chose the HSV histogram and the dominant color as color descriptors. The edge histogram and the nondirectional edge density were chosen as texture descriptors.…”

Section: Data Modelmentioning

confidence: 99%

Population and Query Interface for a Content-Based Video Database

Ardizzone¹,

Cascia²,

Maniscalco

et al. 2002

XML-Based Data Management and Multimedia Engineering — EDBT 2002 Workshops

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Abstract. In this paper we describe the first full implementation of a contentbased indexing and retrieval system for MPEG-2 and MPEG-4 videos. We consider a video as a collection of spatiotemporal segments called video objects; each video object is a sequence of video object planes. A set of representative video object planes is used to index each video object. During the database population, the operator, using a semi-automatic outlining tool we developed, manually selects video objects and insert some semantical information. Lowlevel visual features like color, texture, motion and geometry are automatically computed. The system has been implemented on a commercial relational DBMS and is based on the client server paradigm. The database population client is a stand-alone Windows application; the querying of the system is performed with a web-based client. To test the system we indexed a number of videos taken from Italian soccer championship games. A few examples of queries in this particular domain are reported and results appear very promising.

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Unifying Textual and Visual Cues for Content-Based Image Retrieval on the World Wide Web

Cited by 109 publications

References 39 publications

A Fast Image-Gathering System on the World-Wide Web Using a PC Cluster

A Fast Image-Gathering System on the World-Wide Web Using a PC Cluster

Adaptive relevance feedback for large-scale image retrieval

Population and Query Interface for a Content-Based Video Database

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