MatchNet: Unifying feature and metric learning for patch-based matching. Motivated by recent successes on learning feature representations and on learning feature comparison functions, we propose a unified approach to combining both for training a patch matching system. Our system, dubbed Match-Net, consists of a deep convolutional network that extracts features from patches and a network of three fully connected layers that computes a similarity between the extracted features. To ensure experimental repeatability, we train MatchNet on standard datasets and employ an input sampler to augment the training set with synthetic exemplar pairs that reduce overfitting. Once trained, we achieve better computational efficiency during matching by disassembling MatchNet and separately applying the feature computation and similarity networks in two sequential stages. We perform a comprehensive set of experiments on standard datasets to carefully study the contributions of each aspect of MatchNet, with direct comparisons to established methods. Our results confirm that our unified approach improves accuracy over previous state-of-the-art results on patch matching datasets, while reducing the storage requirement for descriptors. We make pre-trained MatchNet publicly available.
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References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Ma, Jiayi; Jiang, Xingyu; Fan, Aoxiang; Jiang, Junjun; Yan, Junchi: Image matching from handcrafted to deep features: a survey (2021)
- Song, Taeyong; Kim, Youngjung; Oh, Changjae; Jang, Hyunsung; Ha, Namkoo; Sohn, Kwanghoon: Simultaneous deep stereo matching and dehazing with feature attention (2020)
- Axel Barroso-Laguna, Edgar Riba, Daniel Ponsa, Krystian Mikolajczyk: Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters (2019) arXiv
- Mukundan, Arun; Tolias, Giorgos; Bursuc, Andrei; Jégou, Hervé; Chum, Ondřej: Understanding and improving kernel local descriptors (2019)
- Žbontar, Jure; Lecun, Yann: Stereo matching by training a convolutional neural network to compare image patches (2016)