IPTC Controlled Vocabulary

Transcription

1 IPTC Controlled Vocabulary Our business relies on people finding images 1

2 Why have a CV? Order the digital way. 2

3 More questions.. What is a controlled vocabulary? How are they used? Do we need them? Is a standard CV possible? How do CV s benefit our business? The aim of a CV To supply all relevant terms Ensure the terms are unambiguous (Paris location, Paris Hilton, Paris Greek mythology etc) Allow unambiguous translation of terms Make keywording consistent Allow the user to find the image 3

4 What is a controlled vocabulary? A list of standard terms to tag images Can be a simple authority list Can be a hierarchical structure Authority List 4

5 Hierarchical Structure celebration party birthday party cocktail party dinner party girls night housewarming party and so on. Preferred Terms and Synonyms PREFERRED TERM SYNONYMS Dependancy Dependant, Need, Needy 5

6 Preferred Terms and Synonyms PREFERRED TERM SYNONYMS Dependancy Dependant, Need, Needy How synonyms are used will depend on the image library and its search system How does a CV help keywording? 6

7 Words to choose from Supplies broad and narrow terms celebration party birthday party cocktail party dinner party girls night housewarming party and so on. Words to choose from Can apply synonyms Preferred Term party Synonyms parties partying 7

8 Words to choose from Can apply synonyms Preferred Term party Synonyms parties partying Keywording software is essential collect broader terms celebration party birthday party cocktail party dinner party girls night housewarming party and so on. 8

9 Keywording productivity Enter one term and others are supplied automatically ti Broad terms, synonyms, similar terms CV basic rules Relationships should be true is rat a pest? a domestic animal? a pet? A rodent? Which of these is always true? 9

10 CV basic rules You can place rat in a number of places in a hierarchy (homonyms) You need to distinguish between rat (pet) and rat (pest) and rat (source of food)..and any other forms of rat! CV basic rules Terms should be unambiguous orange (fruit) and orange (colour) 10

11 CV basic rules specifications Language (eg English UK) Preferred term capitalisation (Cat or cat) Preferred terms singular or plural (cat or cats) Keywording productivity Keyworders working with same preferred terms, structure t and synonym list 11

12 Keywording productivity Keyworders working with same preferred terms, structure t and synonym list Avoids spelling mistakes Keywording productivity Keyworders working with same preferred terms, structure t and synonym list Avoids spelling mistakes Keeps keywording consistent 12

13 Keywording productivity Keyworders working with same preferred terms, structure t and synonym list Avoids spelling mistakes Keeps keywording consistent Reminder of available terms Orange 13

14 Orange colour Orange fruit 14

15 Orange county California The work has been done behind the scenes by teams of people 15

16 Who uses CV s? Getty, Corbis, Image Source, other large agencies Smaller agencies and photographers are beginning to look at CV s Keywording companies They all realise the task is too difficult without a CV Are there any standards? There are no standards ( Getty standard describes the Getty CV but no one else has it) But there are broad similarities between the CV s in stock industry 16

17 What does the professional image industry offer? Quality images What does the professional image industry offer? Quality images The ability to find them Ideas on how to use them Ways to cut down search time Experience fast, smart, a pleasure to use These result from using a good CV 17

18 Why a standard CV from IPTC? Interoperability Why a standard CV from IPTC? Interoperability Do the thinking upfront 18

19 Why a standard CV from IPTC? Interoperability Do the thinking upfront Encourage good keywording Why a standard CV from IPTC? Interoperability Do the thinking upfront Encourage good keywording Give the industry a tool it needs 19

20 Can a CV describe the world? Yes. Controlled vocabularies originated in text search librarianshipi Modified to make image search practical for the stock industry The structure covers all areas of imagery 20

21 Here are some top levels to start with Objects (orange, teapot) Entities (named people, organsations, events Orange phones, The Orange Order) Broad topics (subject areas organising knowledge) Activities (running, swimming, reading) Abstract concepts (absence, love, goodness) Events (meeting, holiday) Groups (political parties, religious groups ) Description of the object (age, number of people) Description of visual aspects (small, orange) A CV will allow better translation 21

22 Translation Translation what we need Numeric code Baseball bat Baseballschlagholz 22

23 Translation what we need Numeric code 1 Calf (young cow) Kalb Translation what we need Numeric code 1 Calf (young cow) Kalb Numeric code 2 Calf (partof leg) Wade 23

24 There are problems Terms don t always translate well back and forth Language A may have several words for an idea or concept in language B Language A may not have a word for a concept in language B The reality Keywording is not an exact science 24

25 The reality Keywording is not an exact science Retrieval of images is about best approximation The reality Keywording is not an exact science Retrieval of images is about best approximation...and a standard CV would help significantly 25

26 What about visual search? What about visual search? 26

27 What about visual search? Visual Search? Words still need to be added to images in the first place 27

28 Visual Search? Words still need to be added to images in the first place Computers can learn, but they are not emotionally literate (not yet) Visual Search? Words still need to be added to images in the first place Computers can learn, but they are not emotionally literate (not yet) Interpretation is required 28

29 Visual Search? Words still need to be added to images in the first place Computers can learn, but they are not emotionally literate (not yet) Interpretation is required The better the words, the more likelihood of the image being found Visual Search? Words still need to be added to images in the first place Computers can learn, but they are not emotionally literate (not yet) Interpretation is required The better the words, the more likelihood of the image being found Visual search is an aid to image search 29

30 In a global market we need.. Interoperability between agencies Translate terms into other languages once Increased accuracy of keywording Visibility in the mass of images on the web to sell rights What kind of Orange? Orange orange Orange Orange 30

31 Why a standard CV from IPTC? Interoperability Do the thinking upfront Encourage good keywording Give the industry a tool it needs Where did we start? IPTC field for a controlled vocabulary Feasibility issues would there be interest? Encouragement at IPTC Conference in Dresden Realisation we need experts involved 31

32 Where did we start? IPTC field for a controlled vocabulary Feasibility issues would there be interest? Encouragement at IPTC Conference in Dresden Realisation we need experts involved Liisa Kaakinen Stock Industry CV s Can describe entire range of images Are image specific Have been worked on for a number of years No standards, but similarities between schemes Belong to the agencies who created them Have been tested in the real world 32

33 Comparisons NEWSCODES STOCK INDUSTRY CV IPTC CV Created for newstories and Should eventually cover all extended to visual media Created for images media Comparisons NEWSCODES STOCK INDUSTRY CV IPTC CV Created for newstories and Should eventually cover all extended to visual media Created for images media Mainly subject based (aboutness) Includes subject areas, things in the image, and abstract concepts Should cover all these areas 33

34 Comparisons NEWSCODES STOCK INDUSTRY CV IPTCCV CV Created for newstories and extended to visual media Created for images Should eventually cover all media Mainly subject based (aboutness) Includes subject areas, things in the image, and abstract concepts Should cover all these areas Uses different keyword types throughout the structure Tends to divide terms into keywords of the same type Would be good to divide by (with some exceptions) type? Comparisons NEWSCODES STOCK INDUSTRY CV IPTC CV Created dfor newstories and Should eventually cover all extended to visual media Created for images media Mainly subject based (aboutness) Includes subject areas, things in the image, and abstract concepts Should cover all these areas Uses different keyword Tends to divide terms into types throughout the keywords of the same type Would be good to divide by structure (with some exceptions) type? Created to maximise Used to aid keywording and interoperability (uses codes) search Should do both 34

35 Comparisons NEWSCODES STOCK INDUSTRY CV IPTC CV Created dfor newstories and Should eventually cover all extended to visual media Created for images media Mainly subject based (aboutness) Includes subject areas, things in the image, and abstract concepts Should cover all these areas Uses different keyword Tends to divide terms into types throughout the keywords of the same type Would be good to divide by structure (with some exceptions) type? Created to maximise Used to aid keywording and interoperability search Should do both Uses standard machine readable codes Terms exported as keywords (though codes are often Should use machine used in the background) readable codes Keyword Types Things objects visible in the image Entities Broad Topics Activities Abstract Concepts Attributes 35

36 Keyword Types Things objects visible in the image I want an image of a cat FlickR Batabid Keyword Types Things objects visible in the image But I might want to find images on the subject of cats 36

37 Keyword Types Things Entities named people or organisations Broad Topics Activities Abstract Concepts Attributes Keyword Types Entities named people or organisations I want an image of Barak Obama 37

38 Keyword Types Things Entities Broad Topics subject areas, knowledge areas Activities Abstract Concepts Attributes Keyword Types Broad Topics subject areas, knowledge areas I want an image on the subject of Egypt FlickR Unhindered by Talent 38

39 Keyword Types Things Entities Broad Topics Activities actions, doing words Abstract Concepts Attributes Keyword Types Activities actions, doing words I want an image of someone skipping FlickR Robert S.Donovan 39

40 Keyword Types Things Entities Broad Topics Activities Abstract Concepts abstract ideas Attributes Keyword Types Abstract Concepts abstract ideas I want an image illustrating the concept Love FlickR tanakawho 40

41 Keyword Types Things Entities Broad Topics Activities Abstract Concepts Attributes descriptive words Keyword Types Attributes descriptive words I want an image showing a girl in a red shirt FlickR Robert S.Donovan 41

42 Keyword Types Attributes descriptive words I want an image showing a girl with red hair FlickR Meanest Indian Keyword Types Attributes descriptive words It would be good to know if red applies to The hair or the shirt 42

43 Keyword Types Shows object, entity, thing can be seen directly in the image/media This is a picture of a dog shows a dog FlickR Mike Baird Keyword Types Is about about the topic or subject area This is a picture on the theme of education is about education FlickR D-Arcy Norman 43

44 Keyword Types shows featured in the image This picture shows a book Keyword Types This picture expresses the idea of love is about love FlickR Tanakawho 44

45 Keyword Types Has attribute is a descriptive word The person in the image is female has attribute female FlickR Scarleth White Keyword Types Has attribute is a descriptive word The person in the image is happy has attribute happy FlickR Scarleth White 45

46 Keyword Types Has attribute is a descriptive word The dress in the image is green has attribute green FlickR Scarleth White Keyword Types Has attribute is a descriptive word The image is portrait format has attribute portrait FlickR Scarleth White 46

47 Keyword Types Has attribute is a descriptive word The image is about happiness has attribute happiness FlickR Scarleth White Keyword Types Shows featured in the image/media Is about about or expressing the topic or subject area or idea Has attribute is a descriptive word 47

48 RDF Structure Predicate Term Attribute 1 Attribute 2 Attribute 3 This image shows Caucasian Girl Aged white FlickR Pipi Meanest Indiant Indian RDF Structure This image Predicate Term Attribute 1 Attribute 2 Attribute 3 shows Girl Aged Caucasian This image shows hair red 48

49 RDF Structure This image Predicate Term Attribute 1 Attribute 2 Attribute 3 shows Girl Aged Caucasian This image shows hair red This image has attribute Landscape RDF Structure Predicate Term Attribute 1 Attribute 2 Attribute 3 This image shows Girl Aged Caucasian skipping This image Is about health This image shows shirt Red FlickR Robert S. Donovan 49

50 RDF Structure Subject the resource the assertion is made about Predicate the assertion Object the resource that complements the subject Real World Object Real World Entity Concept Attribute Top level terms 50

51 Real World Object Animal Human Plant Man made Object (Building, Equipment) Weather phenomenon Topgraphic feature (mountain) Real world Entity Person (John Lennon) Organisation (CEPIC) Geopolitical areas (Europe, Ireland, Dublin) Named Events (CEPIC Congress) 51

52 Activity (dancing) Concept Emotion (sad) Cultural Styles and periods (17 Century, Baroque) Broad Topics (Education, Media) Attribute (qualifier) Animal attributes (age, gender, groups) Human attributes t (age, ethnicity, it roles) Plant attributes (anatomy, categories) Colours General attributes (large, covered, old, striped) Image attributes (close up, exterior shot) 52

53 The first review We have undertaken a review with a group of keywording experts from Stock Industry Cultural Heritage News How many levels? Issues 53

54 How many levels? Who will be involved? Issues How many levels? Who will be involved? Who will maintain it? Issues 54

55 Issues How many levels? Who will be involved? Who will maintain it? Relationship with Newscodes Issues How many levels? Who will be involved? Who will maintain it? Relationship with Newscodes How will it work with software? 55

56 Feedback We have a draft CV (top levels) A second review process will take place We welcome your comments and involvement