  Architectures based on siamese networks with triplet loss have shown
outstanding performance on the image-based similarity search problem. This
approach attempts to discriminate between positive (relevant) and negative
(irrelevant) items. However, it undergoes a critical weakness. Given a query,
it cannot discriminate weakly relevant items, for instance, items of the same
type but different color or texture as the given query, which could be a
serious limitation for many real-world search applications. Therefore, in this
work, we present a quadruplet-based architecture that overcomes the
aforementioned weakness. Moreover, we present an instance of this quadruplet
network, which we call Sketch-QNet, to deal with the color sketch-based image
retrieval (CSBIR) problem, achieving new state-of-the-art results.
