This document describes several algorithms for calculating relevance scores between queries and documents. It discusses TF-IDF scoring, where term frequency is multiplied by inverse document frequency. It also describes using cosine similarity to calculate image similarity based on visual features. Finally, it discusses combining base ranking scores with image similarity scores.

5. 𝑠𝑐𝑜𝑟𝑒 𝑞, 𝑑 ={
1 𝑖𝑓 𝑑 → 𝑞
0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

6. 𝑠𝑐𝑜𝑟𝑒 𝑞, 𝑑 = 4 log
𝑁
𝑑𝑓9
:
𝑡𝑓9; : (𝑘> + 1)
𝑡𝑓9; + 𝑘> : ( 1 − 𝑏 + 𝑏 :
𝐿;
𝐿DEF
)9 HI J
𝑓HK = frequency of term 𝑖in document 𝑗
𝑡𝑓HK =
𝑓HK
𝑚𝑎𝑥H{𝑓HK}
𝑠𝑐𝑜𝑟𝑒 𝑞, 𝑑 = 4 𝑡𝑓9,;×𝑖𝑑𝑓9
^
9 HI J
𝑑𝑓H = document frequency of term 𝑖
= number of documents containing term 𝑖
𝑖𝑑𝑓H = inverse document frequency of term 𝑖
𝑖𝑑𝑓H = log^(
𝑁
𝑑𝑓H
)

19. 𝑅
𝐶
𝑟H: 𝑖
𝑐K: 𝑗
𝐼𝑚𝑎𝑔𝑒𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦 𝑅, 𝑐K = 4 1 − 𝑐𝑜𝑠𝑖𝑛𝑒 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒(𝑟H, 𝑐K)
I
H
𝑐K

20. 𝑛 𝑚
𝑛
𝑚
𝑛 ∗ 𝑚
𝑠𝑐𝑜𝑟𝑒 𝑞, 𝑖 = 𝐵𝑎𝑠𝑒𝑅𝑎𝑛𝑘𝑖𝑛𝑔 𝑞, 𝑖 + 𝐼𝑚𝑎𝑔𝑒𝑆𝑖𝑚𝑖𝑙𝑎𝑟𝑖𝑡𝑦(𝑅, 𝑖)
< , >

22. 𝐶> = {𝑣>, 𝑣^,…, 𝑣H} 𝐶> = {𝑣>, 𝑣^,…, 𝑣H}
𝑇> = {𝑡>, 𝑡^,…, 𝑡H}
where 𝑡 = {𝑤>, 𝑤^,…, 𝑤s}
w,w,… for C>
1
2
𝑇^ = {𝑡>, 𝑡^,…, 𝑡K}
where 𝑡 = {𝑤>, 𝑤^,…, 𝑤v}
w,w,… for C^

24. 𝑞>
𝑞^
𝑞w
𝑞I
𝑖>
𝑖^
𝑖w
𝑖x

25. ü
ü
ü
ü
ü
ü
ü

26. 𝑐>, 𝑐^, 𝑐w… 𝑐I 𝑐H = (𝑞, 𝑑)
𝑑>, 𝑑^, 𝑑w… 𝑑I 𝑑H = (…, 𝑙)
𝑙 = {1,2,3,4,5,6,7,8}
𝑞D,𝑙 , 𝑞€, 𝑙 , 𝑞D, 𝑙 …(𝑞•, 𝑙)
𝑞D, 𝑙 , 𝑞D, 𝑙 …
𝑞€, 𝑙 , 𝑞€,𝑙 …
𝑙 𝑞

27. ü
ü

28. 𝑙 𝑞

29. 𝑥

30. ü
ü
ü

32. •
•
•

35. ü

36. ü