  Content-Based Image Retrieval based on local features is computationally
expensive because of the complexity of both extraction and matching of local
feature. On one hand, the cost for extracting, representing, and comparing
local visual descriptors has been dramatically reduced by recently proposed
binary local features. On the other hand, aggregation techniques provide a
meaningful summarization of all the extracted feature of an image into a single
descriptor, allowing us to speed up and scale up the image search. Only a few
works have recently mixed together these two research directions, defining
aggregation methods for binary local features, in order to leverage on the
advantage of both approaches. In this paper, we report an extensive comparison
among state-of-the-art aggregation methods applied to binary features. Then, we
mathematically formalize the application of Fisher Kernels to Bernoulli Mixture
Models. Finally, we investigate the combination of the aggregated binary
features with the emerging Convolutional Neural Network (CNN) features. Our
results show that aggregation methods on binary features are effective and
represent a worthwhile alternative to the direct matching. Moreover, the
combination of the CNN with the Fisher Vector (FV) built upon binary features
allowed us to obtain a relative improvement over the CNN results that is in
line with that recently obtained using the combination of the CNN with the FV
built upon SIFTs. The advantage of using the FV built upon binary features is
that the extraction process of binary features is about two order of magnitude
faster than SIFTs.
