A Comprehensive Survey on Cross-modal Retrieval
TABLE IV: MAP comparison with different features on the Wiki dataset.
['Query', 'Methods', 'Features (Image/Text) SIFT/ LDA', 'Features (Image/Text) CNN/ LDA', 'Features (Image/Text) SIFT/ TF-IDF', 'Features (Image/Text) CNN/ TF-IDF']
Results on the Wiki with different types of features: To evaluate the effect of different types of features, we test the cross-modal retrieval performance with different types of features for images and texts on the Wiki dataset. 5000-dimensional feature vectors for texts are extracted by using the bag of words representation with the TF-IDF weighting scheme. PCA is performed on CNN and TF-IDF features for GMLDA and CCA-3V. It can be seen that all methods achieve better results when using the CNN features. This is because CNN features are more powerful for image representation, which has been proved in many fields. Overall, better representation of data leads to better performance.