Previous research on shot-level indexing of moving-image documents identified the terms supplied most often by participants to describe a selection of shots from the National Film Board of Canada's stockshot collection. The most popular terms supplied by participants in the study were compared with the terms assigned by professional indexers for these shots in the source files. Records for some of the shots used in the original study came from the stockshot library's computer database, and the remaining records came from its older card file. Since the level of indexing is specific in the database and more general in the card file, direct comparison is not possible. | However, in both files a high degree of correspondence was found between the most popular terms named by participants in the study and the occurrence of these terms either in the indexing or in the written description of the shots found in the source files. This is encouraging in the context of making collections of moving images available online because it indicates agreement between the terms users think of when searching film and video shots and those indexers assign to them. This suggests that indexing collections of art images at the pre-iconographic (ofness) level in addition to the iconographic (aboutness) level would help improve retrieval rates. |
Shot number | Most popular term | Indexing term assigned | Appears in description as |
---|---|---|---|
1. Computer database | |||
04 | radio | radio | radio |
06 | construct, prefab | construction | prefabricated |
07 | chicken | chikens | chikens |
11 | prairie, field | landscapes | |
17 | winter, snow | winter [time period descriptor] | winter |
18 | lake, aerial, geography | aerial [camera angle descriptor] | aerial |
19 | lake, island | landscapes, rivers | |
21 | stadium | stadiums | stadium |
24 | ship, ferry, ocean, boat | ferries | ferry 06 construct, prefab construction prefabricated |
39 | cat | cats | cat |
42 | airplane, plane, seaplane, transport | seaplanes | seaplane |
2. Card file | |||
01 | flag | ceremonies-NATO forces | flag |
02 | child, family, Inuit | Eskimo1 | |
03 | bird | birds | birds2 |
05 | bird | birds | |
08 | fish, fisherman | fishing, fishermen | fish, fisherman2 |
09 | mountain, rock | mountains | mountain |
10 | ship, box, man, cargo, open, work | ships | ship, men, work |
12 | shop, street, window, store, people | streets, store | shop, shoppers, street, windows |
13 | train, forest, mountain | train, mountains | train, mountains |
14 | grocery, cash | cash | cash |
15 | bird | birds | |
16 | train, freight | trains, freight | train, freight |
20 | winter, snow | winter | winter |
22 | buffalo | buffaloes | buffaloes |
23 | work, man, fish, fisherman, net | fishing | fishermen, nets |
25 | rock, mountain | mountain | |
26 | woodpecker, bird | birds | woodpecker |
27 | old, log | log | log |
28 | building, urban | buildings | buildings |
29 | bird | birds | |
30 | university | universities | university |
31 | duck, bird | ducks, birds | |
32 | fireman, firefighter | firemen | firemen |
33 | moose, forest | [indexing not available]3 | moose, forest |
34 | mountain | [indexing not available]4 | mountain |
35 | sun | sunrises | sunrise |
36 | ski | ski | skiers |
37 | factory, bottle | dairies, food | |
38 | computer | computers | computer |
40 | deer | deer | deer |
41 | native, banquet, Indian, meeting, senior, people, gift, old, elderly | banquets, Indian | |
43 | cow, cattle, farm | farms | cattle |
44 | queen | royalty | queen |
Shots in the database | % | Shots in the card file | % | |
---|---|---|---|---|
Term appears in indexing | 9/11 | 82 | 28/31 | 90 |
Term appears in description | 9/11 | 82 | 25/33 | 76 |