The classic problem of ad hoc information retrieval
involves a user with an information need, a
representation or expression of that information need
(the query), and a system or retrieval engine that
compares the query against a collection of items in
order to return the most relevant items to the user
information need.  Despite numerous and obvious
exceptions, in general text information retrieval has
a fairly high correlation between the syntax of a
query as expressed in language and the semantics of
the information need.  Textual similarity is highly
correlated with relevance.  On the other hand, in
content-based multimedia retrieval (images, video,
music, 3d models), objects encompass multitudinous
semantics in many different dimensions.  In music for
example there are properties of pitch, tempo, rhythm,
timbre, singer characteristics, genre,
instrumentation, year of production, and so on.  The
correlation between similarity and relevance is much
lower.  Two music pieces might be similar because they
both use similar instruments, timbres, tempos and
singers, but they are not necessary both relevant to
my information need if I am looking for waltzes, and
one piece is in 3/4 and the other in 4/4.  

The current popular solution to this problem,
characterized by buzzwords such as "collective
intelligence", "wisdom of crowds" and "Web 2.0", is to
bypass content altogether.  By instead aggregating the
media interactions (playlists, tags, click behavior,
etc.) of massive numbers of people, the collective
intelligence approach hopes to be able to determine
relevance directly, without the need for content-based
methods.  If people are not only the ultimate
consumers, but also the ultimate producers of
relevance, why waste any effort on a problem as
difficult as content-based retrieval?  In our
presentation we reject this notion of complete
reliance on collective intelligence methods and argue
that content-based methods are necessary.  Aggregate
crowd relevance information may be able to tell us
what should be retrieved, but it still will not tell
us why something was retrieved.  For that, we still
need to rely on the explanatory power of content. 
Therefore, we propose the "cognitive disclosure"
paradigm, in which semantic representations are chosen
a priori by designers of a content retrieval system,
i.e. content-features necessary to call a piece of
music a "waltz", or to call an image a "landscape". 
These semantic categories are then revealed to users
at retrieval time, to allow them more intelligent
selection of the types of information that is relevant
to them.  This problem is still very difficult and
there are no easy solutions.  However, our purpose is
simply to explain why "wisdom of crowds" approaches
will inevitably fall short, and content-based methods
are still going to be necessary.