1. 導(dǎo)讀

結(jié)合（卷積）神經(jīng)網(wǎng)絡(luò)的測度學(xué)習(xí)，適用于圖像拼接image stitching、圖像立體匹配 image stereo matching、圖像檢索image retrieval。 當前三個方向都非常火熱，落地項目也很多。

文獻主要來源于實驗室?guī)熜謌alad-loth維護的DeepMatching：https://github.com/galad-loth/DeepMatch。因為我也是一篇篇讀完的，感覺文獻質(zhì)量很高就遷移過來了。

2. 文獻總結(jié)（2015-2017）

2.1 圖像拼接 image stitching

Embedding Learning：

[1] Simo-Serra E, Trulls E, Ferraz L, et al. Discriminative learning of deep convolutional feature point descriptors[C]. Proceedings of the IEEE International Conference on Computer Vision. 2015: 118-126.

[2] Liu Z, Li Z, Zhang J, et al. Euclidean and Hamming Embedding for image patch description with convolutional networks[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2016: 72-78.

[3] Lin K, Lu J, Chen C S, et al. Learning compact binary descriptors with unsupervised deep neural networks[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 1183-1192.

[4] Kumar B G, Carneiro G, Reid I. Learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 5385-5394.

[5] Yang H F, Lin K, Chen C S. Supervised Learning of Semantics-Preserving Hash via Deep Convolutional Neural Networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017. （highlight）

[6] Tian, Yurun, Bin Fan, and Fuchao Wu. "L2-Net: Deep learning of discriminative patch descriptor in euclidean space." Conference on Computer Vision and Pattern Recognition (CVPR). Vol. 2. 2017.

[7] Balntas, Vassileios, et al. "HPatches: A benchmark and evaluation of handcrafted and learned local descriptors." [C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017:

Metric Learning

[1] Zagoruyko S, Komodakis N. Learning to compare image patches via convolutional neural networks[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 4353-4361.

[2] Han X, Leung T, Jia Y, et al. Matchnet: Unifying feature and metric learning for patch-based matching[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 3279-3286.

2.2 圖像立體匹配 image stereo matching

[1] Luo W, Schwing A G, Urtasun R. Efficient deep learning for stereo matching[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 5695-5703.

[2] Zbontar J, LeCun Y. Stereo matching by training a convolutional neural network to compare image patches[J]. Journal of Machine Learning Research, 2016, 17(1-32): 2.

[3] Kn?belreiter P, Reinbacher C, Shekhovtsov A, et al. End-to-End Training of Hybrid CNN-CRF Models for Stereo[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017:

[4] Kendall A, Martirosyan H, Dasgupta S, et al. End-to-End Learning of Geometry and Context for Deep Stereo Regression[C]. Proceedings of the IEEE International Conference on Computer Vision. 2017:

[5] Tulyakov S, Ivanov A, Fleuret F. Weakly supervised learning of deep metrics for stereo reconstruction[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 1339-1348.

2.3 圖像檢索 image retrival

[1] Erin Liong V, Lu J, Wang G, et al. Deep hashing for compact binary codes learning[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 2475-2483.

[2] Zhang Z, Chen Y, Saligrama V. Efficient training of very deep neural networks for supervised hashing[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 1487-1495.

[3] Liu H, Wang R, Shan S, et al. Deep supervised hashing for fast image retrieval[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 2064-2072.

[4] Zhuang B, Lin G, Shen C, et al. Fast training of triplet-based deep binary embedding networks[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016: 5955-5964.

[5] Lu J, Liong V E, Zhou J. Deep Hashing for Scalable Image Search[J]. IEEE Transactions on Image Processing, 2017, 26(5): 2352-2367.

[6] Chen Z, Lu J, Feng J, et al. Nonlinear Sparse Hashing[J]. IEEE Transactions on Multimedia, 2017.

[7] Li Y, Zhang Y, Huang X, et al. Large-scale remote sensing image retrieval by deep hashing neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017.

與50位技術(shù)專家面對面20年技術(shù)見證，附贈技術(shù)全景圖

總結(jié)

以上是生活随笔為你收集整理的DeepMatching文献总结：图像拼接、图像匹配、图像检索的全部內(nèi)容，希望文章能夠幫你解決所遇到的問題。

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