to the $[0,1]$ interval for each attribute, in order to avoid disparities due to different descriptor attribute ranges. We apply majority voting in the scenarios involving an odd number of descriptors, so that each predicted class is taken as the one most frequently predicted by the estimators constructed for each descriptor.

Descriptors and Rankers
-----------------------

For ME17-DIRSM, we selected three visual descriptors and three textual descriptors, for individual analysis in the designed evaluation scenarios, and to evaluate different possibilities of rank-fusion aggregations. We adopt the following state-of-the-art visual descriptors:

-   *ResNet50IN*: 2048-dimensional average pooling of the last convolutional layer of ResNet50 [@he:2016:ResNet50], pre-trained on ImageNet [@Russakovsky:2015:ImageNet], a dataset of about 14M images labeled for object recognition;

-   *VGG16P365*: 512-dimensional average pooling of the last convolutional layer of VGG16 [@simonyan:2014:VGG16], pre-trained on Places365-Standard [@zhou:2017:Places], a dataset of about 10M images of labeled scenes;

-   *NASNetIN*: 2048-dimensional average pooling of the last convolutional layer of NASNet [@barret:2017:NASNet], pre-trained on ImageNet dataset.

Based on the textual metadata in ME17-DIRSM, we adopt the following descriptors:

-   *BoW*: Bag of Words (BoW) with Term Frequency (TF) weighting;

-   *2grams*: 2grams with TF weighting;

-   *doc2vecWiki*: 300-dimensional doc2vec [@le:2014:doc2vec] pre-trained on English Wikipedia dataset, of about 