WWW2009 EPrints

Visual Diversification of Image Search Results

This item is a Paper in the Rich Media track.

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Due to the reliance on the textual information associated with an image, image search engines on the Web lack the discriminative power to deliver visually diverse search results. The textual descriptions are key to retrieve relevant results for a given user query, but at the same time provide little information about the rich image content. In this paper we investigate three methods for visual diversification of image search results. The methods deploy lightweight clustering techniques in combination with a dynamic weighting function of the visual features, to best capture the discriminative aspects of the resulting set of images that is retrieved. A representative image is selected from each cluster, which together form a diverse result set. Based on a performance evaluation we find that the outcome of the methods closely resembles human perception of diversity, which was established in an extensive clustering experiment carried out by human assessors. models deployed on the Web and by these photo sharing sites rely heavily on search paradigms developed within the field Information Retrieval. This way, image retrieval can benefit from years of research experience, and the better this textual metadata captures the content of the image, the better the retrieval performance will be. It is also commonly acknowledged that a picture has to be seen to fully understand its meaning, significance, beauty, or context, simply because it conveys information that words can not capture, or at least not in any practical setting. This explains the large number of papers on content-based image retrieval (CBIR) that has been published since 1990, the breathtaking publication rates since 1997 [12], and the continuing interest in the field [4]. Moving on from simple low-level features to more discriminative descriptions, the field has come a long way in narrowing down the semantic gap by using high-level semantics [8]. Unfortunately, CBIR-methods using higher level semantics usually require extensive training, intricate object ontologies or expensive construction of a visual dictionary, and their performance remains unfit for use in large scale online applications such as the aforementioned search engines or websites. Consequently, retrieval models operating in the textual metadata domain are therefore deployed here. In these applications, image search results are usually displayed in a ranked list. This ranking reflects the similarity of the image’s metadata to the textual query, according to the textual retrieval model of choice. There may exist two problems with this ranking. First, it may be lacking visual diversity. For instance, when a specific type or brand of car is issued as query, it may very well be that the top of this ranking displays many times the same picture that was released by the marketing division of the company. Similarly, pictures of a popular holiday destination tend to show the same touristic hot spot, often taken from the same angle and distance. This absence of visual diversity is due to the nature of the image annotation, which does not allow or motivate people to adequately describe the visual content of an image. Second, the query may have several aspects to it that are not sufficiently covered by the ranking. Perhaps the user is interested in a particular aspect of the query, but doesn’t know how to express this explicitly and issues a broader, more general query. It could also be that a query yields so many different results, that it’s hard to get an overview of the collection of relevant images in the database.

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RKBExplorer (from linked data workshop)
URI: http://eprints.rkbexplorer.com/id/www2009/eprints-35
Browse the data for this paper at RKBExplorer
REST Interface
ORE Resource Map
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About this site

This website has been set up for WWW2009 by Christopher Gutteridge of the University of Southampton, using our EPrints software.

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If you are presenting a paper or poster and have slides or supporting material you would like to have permentently made public at this website, please email cjg@ecs.soton.ac.uk - Include the file(s), a note to say if they are presentations, supporting material or whatnot, and the URL of the paper/poster from this site. eg. http://www2009.eprints.org/128/

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We (Southampton EPrints Project) intend to preserve the files and HTML pages of this site for many years, however we will turn it into flat files for long term preservation. This means that at some point in the months after the conference the search, metadata-export, JSON interface, OAI etc. will be disabled as we "fossilize" the site. Please plan accordingly. Feel free to ask nicely for us to keep the dynamic site online longer if there's a rally good (or cool) use for it...

Fun Stuff

WWW2009 EPrints supports OAI 2.0 with a base URL of http://www2009.eprints.org/cgi/oai2
The JSON URL is http://www2009.eprints.org/cgi/json?callback=function&eprintid=number

To prevent google killing the server by hammering these tools, the /cgi/ URL's are denied to robots.txt - ask Chris if you want an exception made.

Feel free to contact me (Christopher Gutteridge) with any other queries or suggestions. ...Or if you do something cool with the data which we should link to!

Handy Tools

These are not directly related to the EPrints set up, but may be of use to delegates.

Social tool links
I've put links in the page header to the WWW2009 stuff on flickr, facebook and to a page which will let you watch the #www2009 tag on Twitter. Not really the right place, but not yet made it onto the main conference homepage. Send me any suggestions for new links.
When demoing live websites, use this tool to shorten the current URL and make it appaer real big, your audience can then easily type in the short URL and get to the same page as you. Available as a javascript bookmark