  Image search can be tackled using deep features from pre-trained
Convolutional Neural Networks (CNN). The feature map from the last
convolutional layer of a CNN encodes descriptive information from which a
discriminative global descriptor can be obtained. We propose a new
representation of co-occurrences from deep convolutional features to extract
additional relevant information from this last convolutional layer. Combining
this co-occurrence map with the feature map, we achieve an improved image
representation. We present two different methods to get the co-occurrence
representation, the first one based on direct aggregation of activations, and
the second one, based on a trainable co-occurrence representation. The image
descriptors derived from our methodology improve the performance in very
well-known image retrieval datasets as we prove in the experiments.
