Remote Sensing Image Retrieval Based on Regional Attention Mechanism

Yanfei Peng; Jinye Mei; Kaixin Wang; Lingling Zi; Yu Sang

doi:10.3788/LOP57.101017

[1] Lowe D G. Object recognition from local scale-invariant features. [C]∥Proceedings of the Seventh IEEE International Conference on Computer Vision, September 20-27, 1999, Kerkyra, Greece. New York: IEEE, 2, 1150-1157(1999).

[2] Dalal N, Triggs B. Histograms of oriented gradients for human detection. [C]∥2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 20-25, 2005, San Diego, CA, USA, New York: IEEE, 1, 886-893(2005).

[3] Zhao X H, Yin L F, Zhu Y N et al. Improved image classification algorithm based on principal component analysis network[J]. Laser & Optoelectronics Progress, 56, 021004(2019).

[4] Ou P, Zhang Z, Lu K et al. Object detection of remote sensing images based on convolutional neural networks[J]. Laser & Optoelectronics Progress, 56, 051002(2019).

[5] Wang J Q, Li J S, Zhou X W et al. Improved SSD algorithm and its performance analysis of small target detection in remote sensing images[J]. Acta Optica Sinica, 39, 0628005(2019).

[6] Peng Y F, Song X N, Zi L L et al. Remote sensing image retrieval based on convolutional neural network and modified fuzzy C-means[J]. Laser & Optoelectronics Progress, 55, 091008(2018).

[7] Babenko A, Slesarev A, Chigorin A et al. Neural codes for image retrieval[M]. ∥Fleet D, Pajdla T, Schiele B, et al. Computer Vision-ECCV 2014, Lecture Notes in Computer Science. Cham: Springer, 8689, 584-599(2014).

[8] Gordo A, Almazán J, Revaud J et al. End-to-end learning of deep visual representations for image retrieval[J]. International Journal of Computer Vision, 124, 237-254(2017).

[9] Zhou W G, Li H Q, Sun J et al. Collaborative index embedding for image retrieval[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 1154-1166(2018).

[10] Hao J D, Dong J, Wang W et al[2019-09-02]. What is the best practice for CNNs applied to visual instance retrieval? [2019-09-02].https:∥arxiv., org/abs/1611, 01640.

[11] Noh H, Araujo A, Sim J et al. Large-scale image retrieval with attentive deep local features. [C]∥2017 IEEE International Conference on Computer Vision, October 22-29, 2017, Venice Italy. New York: IEEE, 3456-3465(2017).

[12] Teichmann M, Araujo A, Zhu M L et al. Detect-to-retrieve: efficient regional aggregation for image search. [C]∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 15-20, 2019, Long Beach, CA, USA. New York: IEEE, 5109-5118(2019).

[13] Kim J, School of Computing. 209[2019-09-02]. http:∥sglab.kaist.ac.kr/RegionalAttention/.(2018).

[14] Imbriaco R, Sebastian C, Bondarev E et al. Aggregated deep local features for remote sensing image retrieval[J]. Remote Sensing, 11, 493(2019).

[15] Jégou H, Douze M, Schmid C et al. Aggregating local descriptors into a compact image representation. [C]∥2010 IEEE Computer Society Conference on Computer Vision & Pattern Recognition, June 13-18, 2010, San Francisco, CA, USA. New York: IEEE, 3304-3311(2010).

[16] Tolias G, Sicre R, Jégou H[2019-08-25]. Particular object retrieval with integral max-pooling of CNN activations [2019-08-25].https:∥arxiv., org/abs/1511, 05879.

[17] Hoffer E, Banner R, Golan I et al[2019-08-30]. Norm matters: efficient and accurate normalization schemes in deep networks [2019-08-30].https:∥arxiv., org/abs/1803, 01814.

[18] Jégou H, Chum O. Negative evidences andCo-occurences in image retrieval: the benefit of PCA and whitening[M]. ∥Fitzgibbon A, Lazebnik S, Perona P, et al. Computer Vision-ECCV 2012, Lecture Notes in Computer Science. Springer, Berlin, Heidelberg, 7535, 774-787(2012).

[19] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).

[20] Yang Y, Newsam S. Bag-of-visual-words and spatial extensions for land-use classification[C]∥Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, November 2-5, 2010,, 270-279(2010).

[21] Zhao B, Zhong Y F, Xia G S et al. Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 2108-2123(2016).

[22] Zhao B, Zhong Y F, Zhang L P et al. The fisher kernel coding framework for high spatial resolution scene classification[J]. Remote Sensing, 8, 157(2016).

[23] Zhu Q Q, Zhong Y F, Zhao B et al. Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery[J]. IEEE Geoscience and Remote Sensing Letters, 13, 747-751(2016).

[24] Simonyan K, Zisserman A[2019-08-28]. Very deep convolutional networks for large-scale image recognition [2019-08-28].https:∥arxiv., org/abs/1409, 1556.

[25] Huang G. Liu Z,van der Maaten, L, et al. Densely connected convolutional networks. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition, July 21-26, 2017, Honolulu, HI,USA. New York: IEEE, 4700-4708(2017).