  This paper presents a novel approach for image retrieval and pattern spotting
in document image collections. The manual feature engineering is avoided by
learning a similarity-based representation using a Siamese Neural Network
trained on a previously prepared subset of image pairs from the ImageNet
dataset. The learned representation is used to provide the similarity-based
feature maps used to find relevant image candidates in the data collection
given an image query. A robust experimental protocol based on the public
Tobacco800 document image collection shows that the proposed method compares
favorably against state-of-the-art document image retrieval methods, reaching
0.94 and 0.83 of mean average precision (mAP) for retrieval and pattern
spotting (IoU=0.7), respectively. Besides, we have evaluated the proposed
method considering feature maps of different sizes, showing the impact of
reducing the number of features in the retrieval performance and
time-consuming.
