|INPUT||A list of objects D,
and the corresponding relevance scores Y assigned by a baseline model (either for visual search or concept detection).
We assume that feature extractions (e.g., concept detections ) for each visual object are computed in advance.
For these N objects in D, the corresponding M-dimensional features can form an N¡ÑM feature matrix X.
|STEP 1.||Concept selection: wc-tf-idf, an improved feature selection measurement of c-tf-idf .
|STEP 2.||Employment of ranking algorithms: Randomly partition the data set into F folds.
Hold out one fold of data as test set and train the ranking algorithm (such as ListNet  or RankSVM, ) using the remaining data.
Predict the new relevance scores of the test set. Repeat until each fold is held out for testing once.
|STEP 3.||Rank aggregation: Linearly fuse the initial relevance scores and newly predicted scores.
|OUTPUT||Sort the fused scores to output a new ranked list for the target semantics.
(last update: 2008/9/15)