1. A method for multimedia information retrieval comprising: querying a multimedia collection with a first component of a multimedia query to generate first comparison measures between the first query component and respective objects in the collection for a first media type; querying at least a part of the multimedia collection with a second component of the multimedia query to generate second comparison measures between the second query component and respective objects in the collection for a second media type; computing an aggregated score for each of a set of objects in the collection based on the first comparison measure and the second comparison measure for the object, the computing comprising applying an aggregating function to the first and second comparison measures in which a first confidence weighting is applied to the first comparison measure and a second confidence weighting is applied to the second comparison measure, the first confidence weighting is independent of the second comparison measure and the second confidence weighting is dependent on the first comparison measure; and outputting information based on the aggregated scores, wherein the aggregated score is computed using an aggregating function of general format: 
 s cw ( q,o )= A ( N ( s a ( q,o )ƒ), N ( s b ( q,o ) g ))  (3) where S cw , is the aggregated score; A represents the aggregating function; a represents the first media type; b represents the second media type; s a (q,o) and s b (q,o) are similarity scores between the query q and the object o for the first and second media types respectively; r a (o,q) and r b (o,q) are rankings of the object o given by the respective similarity scores s a (q,o) and s b (q,o), with respect to other objects in the collection; ƒ is a function of at least one of r a (o,q) and s a (q,o) and optionally also of θ a ; g is a function of at least one of r a (o,q) and s a (q,o) and at least one of s b (q,o) and r b (o,q) and optionally also of θ b ; θ a is a set of one or more parameters; θ b is a set of one or more parameters; and N represents an optional normalizing operator, wherein at least one of: f ⁡ ( r t ⁡ ( o , q ) ) = g ⁡ ( r t ⁡ ( o , q ) ) = { 1 if ⁢ ⁢ r t ⁡ ( o , q ) ≤ K 0 otherwise ; f ⁡ ( r t ⁡ ( o , q ) ) = g ⁡ ( r t ⁡ ( o , q ) ) = { log ⁡ ( K - r t ⁡ ( o , q ) + 1 ) log ⁡ ( K ) if ⁢ ⁢ r t ⁡ ( o , q ) < K 0 otherwise ; f ⁡ ( r t ⁡ ( o , q ) ) = g ⁡ ( r t ⁡ ( o , q ) ) = { x ⁡ ( K + 1 ) ( K + r t ⁡ ( o , q ) ) - y if ⁢ ⁢ r t ⁡ ( o , q ) < K 0 otherwise ; and f ⁡ ( r t ⁡ ( o , q ) ) = g ⁡ ( r t ⁡ ( o , q ) ) = { s t ⁡ ( o , q ) δ if ⁢ ⁢ r t ⁡ ( o , q ) < K 0 otherwise is applied in the aggregating function, where K represents a number of retrieved objects, and x, y and δ are each a parameter whose value is preselected.