Input: training set with label vocabulary , test set and query with multi-concept scene
Output: ranked search result
1  Compute a set of multi-concept scene by Eqs. (1) and (2);
2  Train our CNN and obtain multi-concept scene classifier and single-concept classifier ;
3  Construct detection context ;
4 for  each    do
5  Detect image concepts using classifier and compute relevance prediction by Eq. (8);
6  Detect image concepts using classifier and compute relevance prediction by Eq. (9);
7  Perform relevance prediction fusion of A and B, and compute final prediction by Eqs. (15) and (16);
8 end
9  Perform heap sort over all predictions for obtaining top- images;
10  Output the image list that stands for the search result ;
Algorithm 1: Semantic image retrieval process.