Comprehensive and Relevant Image Search
by FotoTagger
Why Relevant Image Results Matter
Search engine companies are seeking the ways to improve image search. The
more relevant content found, the more precise binding of ads to content can be
performed. However, finding a relevant image is not an easy task because
currently search engines can operate with very limited information about images
located in the Web.
In our days, the Internet is not only about Web pages. The best answer when a
user performs a search, as Sergey Brin, the Google co-founder, noted on a
conference call outlining third-quarter results, is not always a Web page.
Although Brin applied it to video, it is obvious that an ability to find the
image with content a user needs is the challenge that search engines have to
meet.
Yahoo! Image Search
Better search results mean better ad targeting. Binding ads to search results
is what makes search engine giants worry. Yahoo! reported that they are not
satisfied with their third quarter financial performance. "We continued to
grow and believe that we outperformed the graphical market but not at a rate
that met our expectations," said Terry Semel, chairman and CEO of Yahoo!
Disappointing results were caused by low sales of online ads for cars and
financial services. Analysts say that Yahoo! still fails in precise tailoring of
search results to ads and therefore do not meet advertisers´ expectations. In
Yahoo!, they understand it very well and are looking for new ways to find more
content and improve context advertising. Yahoo!´s efforts to integrate search
results with images from Flickr is an excellent proof.
This is when the real problem comes. While it is not difficult to index
images that are already tagged with Flickr tools, pictures that are not shared
through Flickr (it is pretty obvious to assume that their number is incomparably
more) cannot be achieved as effectively as Flickr photos.
Google Image Search
Google has developed its own way to tag images placed somewhere in the Web
and not necessarily in Google-owned Picasa. In September 2006, Google released
the Google Image Labeler game () that
should help the search engine improve its search results through quality
tagging. During the game, a user is randomly paired with a partner who is
another Web surfer. Over a 90-second period, both users are shown the same set
of images and asked to provide as many labels as possible to describe each image
you see. For example, when seeing an image that shows the Real Madrid football
team, a user can offer labels "football", "Beckham", "players" and
so on. When the label offered by one user matches partner´s label, the user earn
some points and move on to the next image until the game is over.
Luis von Ahn, the Carnegie Mellon Institute professor, who is standing behind
the Google game concept, thinks that in two months, all images on Google Images
could be labeled. As a result, it is expected that image search will become more
precise.
Although this is the great idea and good implementation, the main problem is
that the minimum unit of content is still image as a whole rather than
individual element of the image content. If you succeed to find the group photo
of Manchester United by using "Beckham" as a search word, how do you know
which of them is Beckham? While a traditional text search enables highlighting
the word you are looking for, there is no way to highlight the individual object
that matches the search request unless a tag which clearly and unambiguously
refers to this individual object is assigned.
An ability to work with images on a more granular level would provide much
more opportunities both for search engines, which are interested to find as many
relevant search results as possible, and image owners who are interested to
bring more relevant traffic to their Websites.
For search engines it is about finding more relevant image content and
referring to found objects more precisely. In terms of revenue, it means finding
more specific results that can be used for more precise ads targeting. The more
specific objects are tagged on an image, the more opportunities for advertisers
can be offered. For example, a photo that shows Mercedes and labeled with tags
like "Mercedes", "car", and "auto" can be associated with
advertising of a car dealer. However, if individual elements, such as tires or
car alarm system, are tagged and highlighted depending on the search request,
ads of manufacturers of tires and alarm systems can be also shown.
For image owners it means making images easily reachable by search engines,
increasing ranking level, and therefore attracting more relevant traffic.
Object-specific tagging would take a few minutes that would be even less and
easier than developing metatags for search engine optimization. Moreover, an
ability to define individual elements on images provide more opportunities for
SEO because object-specific tagging allows Website owners to go beyond
traditional meta descriptions and put more information into a single image.
Although image labeling proposed by Google is a great step forward, the
further direction should be defined right away. Image labeling should now go
down to a more granular level and let search engines work with picture objects
rather than with images as a whole.
posted on Jun 25, 2007
Article
Topics
Google