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CBIR_LeaderBoard
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LeaderBoard for various CBIR models
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# CBIR_LeaderBoard LeaderBoard for various CBIR models Content-based Image Retrieval models. Any suggestion for new benchmark dataset is welcome. Any suggestion for missing models is welcome. Most models are based on publicaly published result (peer-reviewd) or reproducable result (with source-code). If you have your model that is not published yet, and not open-sourced, I'll mark it in `etc` column. Rank is based on Oxford105k (mid-scale image retrieval).:trophy: # Oxford 5k, Paris 6k, Oxford 105k, Paris 106k, Holidays (mAP) <table class="blueTable"> <thead> <tr> <th>model</th> <th>oxf5k</th> <th>par6k</th> <th>oxf105k</th> <th>par106k</th> <th>holidays</th> <th>yymm</th> <th>ref</th> <th>etc</th> </tr> </thead> <tbody> <tr> <td>:trophy:GeM(Res)</td> <td>87.8</td> <td>92.7</td> <td>84.6</td> <td>86.9</td> <td>93.9</td> <td>17.11</td> <td>[GeM17]</td> <td> </td> </tr> <tr> <td>GeM(VGG)</td> <td>87.9</td> <td>87.7</td> <td>83.3</td> <td>81.3</td> <td>89.5</td> <td>17.11</td> <td>[GeM17]</td> <td> </td> </tr> <tr> <td>R-MAC(Res,E2E)</td> <td>86.1</td> <td>94.5</td> <td>82.8</td> <td>90.6</td> <td>94.8</td> <td>16.10</td> <td>[DeepIR16]</td> <td> </td> </tr> <td>BoW(200k)+VV</td> <td>80.1</td> <td>73.4</td> <td>74.5</td> <td>64.9</td> <td>-</td> <td>16.xx</td> <td>[VV16]</td> <td>HesAff+RootSIFT, HE, VBW, Top1000, 1-to-1</td> </tr> <td>BoW(200k)</td> <td>76.2</td> <td>71.2</td> <td>66.4</td> <td>60.2</td> <td>-</td> <td>16.xx</td> <td>[VV16]</td> <td> </td> </tr> <td>BoW(16M,L16)+FSM</td> <td>74.2</td> <td>74.9</td> <td>67.4</td> <td>67.5</td> <td>74.9</td> <td>12.xx</td> <td>[VW16M12]</td> <td>HesAff+SIFT, 15 alt.words</td> </tr> <td>BoW(1M)+FSM</td> <td>66.4</td> <td>-</td> <td>54.1</td> <td>-</td> <td>-</td> <td>07.xx</td> <td>[FSM07]</td> <td>HesAff+SIFT</td> </tr> <td>BoW(1M)</td> <td>61.8</td> <td>-</td> <td>49.0</td> <td>-</td> <td>-</td> <td>07.xx</td> <td>[FSM07]</td> <td> </td> </tr> </tbody> </table> * This result does not use Query Expansion (QE), Database Augmentation (DBA), or Spatial Verification. * For BoW based Image Retrieval System, Spatial Verifiaction is necessary to consider spatial information. So, I explicitly add the spatial verification method after `+` symbol. (i.e FSM, VV) * HesAff: Hessian Affine Keypoint Detector. See [HesAff09] * HE: Hamming Embedding (mitigate quantizatin error of visual words). See [HE08] * RootSIFT: practical tip. better represenation for L2 distance measure. See [RootSIFT12] * VBW: Visual Burstiness Weighting (mitigate repetative pattern dominancy problem). See [VBW09] * TopXXX: Rerank top xxx results with spatial verification * 1-to-1 : enforcing 1-to-1 correspondence with keypoint geometry. See [PGM15] [GeM17]: Fine-tuning CNN Image Retrieval with No Human Annotation by Filip Radenović, Giorgos Tolias, Ondřej Chum https://arxiv.org/abs/1711.02512, [DeepIR16]: End-to-end Learning of Deep Visual Representations for Image Retrieval by Albert Gordo, Jon Almazan, Jerome Revaud, Diane Larlus https://arxiv.org/abs/1610.07940 [VV16]: A Vote-and-Verify Strategy for Fast Spatial Verification in Image Retrieval by Sch\"{o}nberger, Johannes Lutz and Price, True and Sattler, Torsten and Frahm, Jan-Michael and Pollefeys, Marc https://github.com/vote-and-verify/vote-and-verify [RootSIFT12]: Three things everyone should know to improve object retrieval by Relja Arandjelovi´c Andrew Zisserman https://www.robots.ox.ac.uk/~vgg/publications/2012/Arandjelovic12/arandjelovic12.pdf [PGM15]: Pairwise Geometric Matching for Large-scale Object Retrieval by Xinchao Li, Martha Larson, Alan Hanjalic https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Li_Pairwise_Geometric_Matching_2015_CVPR_paper.pdf [VW16M12]: Learning Vocabularies over a Fine Quantization by Andrej Mikul´ık, Michal Perdoch, Ondˇrej Chum, and Jiˇr´ı Matas http://cmp.felk.cvut.cz/~perdom1/papers/mikulik_ijcv12.pdf [HesAff09]: Efficient Representation of Local Geometry for Large Scale Object Retrieval by Perdoch, M. and Chum, O. and Matas, J. http://cmp.felk.cvut.cz/~perdom1/hesaff/ [VBW09]: On the burstiness of visual elements by Herve Jegou ; Matthijs Douze ; Cordelia Schmid http://ieeexplore.ieee.org/abstract/document/5206609/ [HE08]: Hamming embedding and weak geometric consistency for large scale image search by Herve Jegou, Matthijs Douze, and Cordelia Schmid https://hal.inria.fr/inria-00316866/document/ [FSM07]: Object retrieval with large vocabularies and fast spatial matching by James Philbin ; Ondrej Chum ; Michael Isard ; Josef Sivic ; Andrew Zisserman http://ieeexplore.ieee.org/document/4270197/ # SIFT Meets CNN: A Decade Survey of Instance Retrieval (last update 17.05) by Liang Zheng, Yi Yang, Qi Tian https://arxiv.org/abs/1608.01807  # INSTRE Dataset Image from "Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations" by Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Teddy Furon, Ondrej Chum https://arxiv.org/abs/1611.05113  # TODO [ ] Add post-processed version including QE, and diffusion.
