This is simple python re-implementation of the algorithms from papers Iscen.et.al "Fast Spectral Ranking for Similarity Search", CVPR2018 and Iscen et.al "Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations" CVPR 2017.

It is NOT authors implementation and some parts, e.g. sparsification, truncation, etc. are missing.

Example of usage: copy files into python directory of the RevisitOP benchmark and run

python example_evaluate_with_diff.py

Expected output:

Plain
>> roxford5k: mAP E: 84.81, M: 64.67, H: 38.47
>> roxford5k: [email protected][ 1  5 10] E: [ 97.06  85.29  70.59], M: [ 97.14  82.86  64.29], H: [ 81.43  31.43  22.86]
Conjugate gradient
>> roxford5k: mAP E: 86.42, M: 72.52, H: 48.56
>> roxford5k: [email protected][ 1  5 10] E: [ 92.65  91.18  82.35], M: [ 92.86  87.14  75.71], H: [ 87.14  41.43  27.14]
Spectral K=100, R=2000
>> roxford5k: mAP E: 86.5, M: 72.0, H: 45.7
>> roxford5k: [email protected][ 1  5 10] E: [ 94.12  91.18  80.88], M: [ 94.29  82.86  70.  ], H: [ 81.43  41.43  22.86]

Manifold Diffusion

Diffusion on manifolds for image retrieval

Manifold Diffusion Info

⭐ Stars114
🔗 Source Codegithub.com
🕒 Last Updatea year ago
🕒 Created5 years ago
🐞 Open Issues0
➗ Star-Issue RatioInfinity
😎 Authorducha-aiki