# Motivation

## Challenges:

- Retrieve all the images depicting the same landmark regardless of visual similarity.
- Ranking is necessary for the MAP metric.
  
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## Iterative DELF Query Expansion Motivation

Put images with the same landmark closer to the approximated centers of the landmark clusters iteratively.

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- Involve local descriptors and geometric verification.
- Test images can be used as additional bridging.

## Google Landmark Retrieval Challenge:

- Largest public dataset for image retrieval.
- 15K unique landmarks, 1M training images, 1M index images, 100K test images.
- Images have various sizes and high resolution, 329GB in total.
- Single model without ensemble.

## Validation:

- No fine-tuning.

## Highlights:

- No fine-tuning.

## Experiment Results:

| Method                        | PubMAP |
|-------------------------------|--------|
| DIR                           | 42.3%  |
| DIR + ΔQE                     | 47.9%  |
| DIR + ID-QE                  | 55.7%  |
| DIR + ID-QE + MMST-C^       | 62.7%  |

## Competition Results:

| Team                         | PvtMAP |
|------------------------------|--------|
| 1. CVS5P & Visual Atoms      | 62.7%  |
| 2. Layer 6 AI               | 60.8%  |
| 3. SevenSpace               | 59.8%  |
| 4. Naveer Labs Europe       | 58.6%  |
| 5. VPP                      | 58.3%  |

- Scalability: fast approximate update for both new images in index and in test.
- Flexibility: limit depth to constrain visual similarity.
- Iterative DELF QE constraints the global feature space.
- Clustering resolves the challenge of visually not similar images through transition of bridging images.
- Modified maximum spanning tree algorithm ranks the candidates across the connected images.

1. Noh H et al. Large-Scale Image Retrieval with Attentive Deep Local Features. Proc. ICCV 2017.
2. Gordo A et al. Deep Image Retrieval: Learning Global