평가 방법

[glee1228]

Evaluation 방법

reference : stanford cs276 Introduction of Information Retrieval Evaluation part
By glee1228@naver.com

5k Dataset Download Link : http://www.robots.ox.ac.uk/~vgg/data/oxbuildings/

1. 데이터셋 구성(train)

루트 디렉토리 이름 : oxbuild_images

• train

  • AllSouls_Oxford(133 pics)
  • Balliol_Oxford(155 pics)
  • ChristChurch_Oxford(544 pics)
  • Hertford_Oxford(68 pics)
  • Jesus_Oxford(162 pics)
  • Keble_Oxford(122 pics)
  • Magdalen_Oxford(686 pics)
  • New_Oxford(459 pics)
  • Oriel_Oxford(96 pics)
  • Trinity_Oxford(218 pics)
  • RadcliffeCamera_Oxford(283 pics)
  • Cornmarket_Oxford(60 pics)
  • Bodleian_Oxford(215 pics)
  • PittRivers_Oxford(109 pics)
  • Ashmolean_Oxford(196 pics)
  • Worcester_Oxford(71 pics)

• 레이블 없는1503 pics

성능 평가 방법

• Precision@K (P@K)
• Mean Average Precision (MAP)

Precision@K

1. Set a rank threshold K

2. Compute % relevant in top K

3. Ignores documents ranked lower than K

Ex: True, False, True, False, True

Prec@3 of 2/3

Prec@4 of 2/4

Prec@5 of 3/5

Mean Average Precision

1. Consider rank position of each relevant doc

2. Compute Precision@K for each K_1, K_2, … , K_R

3. Average precision = average of P@K

Ex: True, False, True, False, True

이 결과의 MAP = 1/3*(1/1+2/3+3/5) :=0.76

4. MAP is Average Precision across multiple
   queries/rankings

Average Precision example

MAP example

![MAP_example]