Combining Convolutional Neural Network and Markov Random Field for Semantic Image Retrieval

Algorithm 1

Semantic image retrieval process.

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 ;