Image retrieval using multi-scale CNN features pooling
Federico Vaccaro, Marco Bertini, Tiberio Uricchio, Alberto Del Bimbo
Abstract
In this paper, we address the problem of image retrieval by learning images representation based on the activations of a Convolutional Neural Network. We present an end-to-end trainable network architecture that exploits a novel multi-scale local pooling based on NetVLAD and a triplet mining procedure based on samples difficulty to obtain an effective image representation. Extensive experiments show that our approach is able to reach state-of-the-art results on three standard datasets.
Topics & Concepts
PoolingComputer scienceConvolutional neural networkArtificial intelligenceRepresentation (politics)Pattern recognition (psychology)ExploitImage (mathematics)Scale (ratio)Image retrievalFeature extractionComputer visionPolitical sciencePoliticsLawQuantum mechanicsPhysicsComputer securityAdvanced Image and Video Retrieval TechniquesImage Retrieval and Classification TechniquesMultimodal Machine Learning Applications