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Discriminative learning of deep convolutional feature point descriptors

info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/3.0/es/ ;DOI: 10.1109/ICCV.2015.22

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  • Title:
    Discriminative learning of deep convolutional feature point descriptors
  • Author: Simó Serra, Edgar ; Trulls Fortuny, Eduard ; Ferraz, Luis ; Kokkinos, Iasonas ; Fua, Pascal ; Moreno-Noguer, Francesc
  • Subjects: Automàtica i control ; Classificació INSPEC ; computer vision ; deep learning ; feature extraction ; Informàtica ; Pattern recognition ; Àrees temàtiques de la UPC
  • Description: Deep learning has revolutionalized image-level tasks such as classification, but patch-level tasks, such as correspondence, still rely on hand-crafted features, e.g. SIFT. In this paper we use Convolutional Neural Networks (CNNs) to learn discriminant patch representations and in particular train a Siamese network with pairs of (non)corresponding patches. We deal with the large number of potential pairs with the combination of a stochastic sampling of the training set and an aggressive mining strategy biased towards patches that are hard to classify. By using the L2 distance during both training and test- ing we develop 128-D descriptors whose euclidean distances reflect patch similarity, and which can be used as a drop-in replacement for any task involving SIFT. We demonstrate consistent performance gains over the state of the art, and generalize well against scaling and rotation, perspective transformation, non-rigid deformation, and illumination changes. Our descriptors are efficient to compute and amenable to modern GPUs, and are publicly available. Peer Reviewed
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • Creation Date: 2015
  • Language: English
  • Identifier: DOI: 10.1109/ICCV.2015.22
  • Source: Recercat
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