2015年12月13日
Discriminative Learning of Deep Convolutional Feature Point Descriptors
International Conference on Computer Vision (ICCV)
- ,
- ,
- ,
- ,
- ,
- 巻
- 2015
- 号
- 開始ページ
- 118
- 終了ページ
- 126
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1109/ICCV.2015.22
- 出版者・発行元
- Institute of Electrical and Electronics Engineers Inc.
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 testing 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.
- リンク情報
- ID情報
-
- DOI : 10.1109/ICCV.2015.22
- ISSN : 1550-5499
- SCOPUS ID : 84973915418