10. A method implemented by a computer system, the method for detecting at least a first concept in an image record, and the method comprising: producing a first detection score relating to said first concept based upon a detection performed by an individual detector that detected said first concept; automatically determining, using a computer system, a plurality of other concepts in said image record which have a contextual relationship with said first concept and which are to be labeled by a user by automatically determining a subset of concepts to be labeled by a user from a larger set of concepts according to the following selection criteria: (a) a best concept to be labeled from a particular plurality of concepts has a highest mutual information with the other concepts from the particular plurality of concepts; and (b) successive optimal concepts to be labeled are those having the largest mutual information with remaining concepts from the particular plurality of concepts and low conditional entropy relative to the other concepts from the plurality of concepts; accepting a label for each of said plurality of other concepts from the user via an input device; producing a second detection score at least by processing the labels using a context-based classifier; linearly combining said first and second detection scores to produce a refined detection score relating to said first concept in said image record; and storing information pertaining to the refined detection score in a computer-readable memory.
10. A method implemented by a computer system, the method for detecting at least a first concept in an image record, and the method comprising: producing a first detection score relating to said first concept based upon a detection performed by an individual detector that detected said first concept; automatically determining, using a computer system, a plurality of other concepts in said image record which have a contextual relationship with said first concept and which are to be labeled by a user by automatically determining a subset of concepts to be labeled by a user from a larger set of concepts according to the following selection criteria: (a) a best concept to be labeled from a particular plurality of concepts has a highest mutual information with the other concepts from the particular plurality of concepts; and (b) successive optimal concepts to be labeled are those having the largest mutual information with remaining concepts from the particular plurality of concepts and low conditional entropy relative to the other concepts from the plurality of concepts; accepting a label for each of said plurality of other concepts from the user via an input device; producing a second detection score at least by processing the labels using a context-based classifier; linearly combining said first and second detection scores to produce a refined detection score relating to said first concept in said image record; and storing information pertaining to the refined detection score in a computer-readable memory. 12. The method of claim 10 wherein said automatically determining includes minimizing an average Bayes classification error for concept detection.