Image Indexing Project Group Indexing Activities and Final Report Group Four Olga Grushin, Brian McGraw and Collette Spence INFO 622 Winter 2010

Transcription

1 Image Indexing Project Group Indexing Activities and Final Report Group Four Olga Grushin, Brian McGraw and Collette Spence INFO 622 Winter 2010 March 16, 2010

2 Table of Contents Introduction... 3 Dublin Core metadata structure... 3 Group Activities... 5 Choosing Terms and Justifying Selections... 5 Individual terms... Error! Bookmark not defined. Collective Terms... Error! Bookmark not defined. Assignment Assessment/Collaborative Environment... 6 Lessons Learned... 6 Summary... 7 Appendix... 7 References... 7

3 Introduction In this assignment we were tasked to index a group of images. The images captured various themes and places as well as geographic locations. There was a two step process to this task. First, we indexed the images individually without any assistance from other group members. Second, as a team we organized the terms that were contributed individually and created consensus group of terms for each image. We also had to map the elements of the Dublin Core standard to a chosen image. Although the subject terms for the Dublin Core are not exact matches to the terms used for indexing the images, we modified our records to show that. Dublin Core metadata structure The Dublin Core Metadata Element Set is a vocabulary of 15 terms used to describe a resource. The terms are as follows: Contributor Coverage Creator Date Description Format Identifier Language Publisher Relation Rights Source Subject Title Type Our task was to map the existing image database fields to the Dublin Core (DC) standard. There were a number of instances where there was a clear relationship between metadata terms, such as with "title" and "ID/Identifier". With other terms in the image indexing database, the relationship with DC terms was not as obvious. Often, two or more existing metadata terms could be combined into a single DC metadata term. One example is with the "Country" and "City" terms. In the Dublin Core standard the only term that exists to specify geographical location is "Coverage". Therefore, the former two terms must be combined. Pictured below is the crosswalk between the existing image database metadata terms, and their DC equivalents. In the table, we have included a brief explanation of the relationship between terms.

4 Example The following image from the image database is titled, "Chinese lawyer sitting with a straw fan" Here is the record for the image, using the existing metadata structure: ID: 20 Title: Chinese lawyer sitting with a straw fan Text on the Front: A chinese lawyer Text on the Back: lawyer-china-shanghai Country: China City: Shanghai

5 Subject TGM: Subject AAT: Owner: Notes: People, men, portraits Lawyer Free Library of Philadelphia Elder Chinese layer sitting at a table. And below, the equivalent Dublin Core record: Identifier 20 Title Description Coverage Subject Source A chinese lawyer A chinese lawyer. lawyer-china-shanghai. Elder Chinese layer sitting at a table. Shanghai, China Men, Lawyers, Interiors, Portraits, Fans, Chinese styles Free Library of Philadelphia Group Activities Choosing Terms and Justifying Selections From the terms that were provided in the individual indexing assignment, we discussed each image and all the contributed terms. We then chose the most relevant terms that were narrow in their descriptions of the image, the objects within the image as well as what the image was about. Using the references from Jorgenson, Aitchison and other authors proved helpful in completing this task. As we are of different academic disciplines and experiences, one can argue that there is some level of subjectivity with the terms that were chosen. However, we decided to use terms that would be more familiar and more likely to be used as a search term. By doing so, we believe this enhances the retrievability of the image indexed. We used a simple spreadsheet to keep track of terms used, context and reasons for term selection. How does one decide if a term is index worthy? Do we apply listed scientific terms to an object in the image? We had some difficulty with this step of the assignment. As stated by Aitchison, we (humans) find some words to be more basic and more descriptive than others. As such, we searched the AAT and TGM thesaurus tools heavily to ensure that the terms we chose were indeed basic and descriptive of each image. However, there were instances where the listed preferred term was not a basic term that would be reflected in the real world. Consequently, we decided to choose terms from the thesaurus that were narrow and specific. Although we initially thought there would be issues with our individual subjectivity, we had no such troubles. In fact, we were able to quickly decide on agreed-upon terms. Applying the concepts that we learned throughout this course and referring back to the lectures was very helpful. Nevertheless, there

6 were a few images that were unfamiliar to us and required some research. As such, we spent more time on those images in order to ensure that we applied relevant index terms. For instance, the images of the Hindu temple as well as some of the architecture images presented in the assignment contain objects that are specific to other cultures. Deciding whether to use terms that were broad or narrow was one of our main focus. We wanted to be able to add terms that were indicative of what the image was, while at the same time refer to what cultural aspects of that image. The attached spreadsheet lists the TGM and AAT terms that were assigned by individual team members, the agreed-upon terms and comments. Assignment Assessment/Collaborative Environment To complete this assignment we utilized the Blackboard chat feature in addition to the offered Google tools. While the Blackboard feature was not a fit for our collaborative needs, the Google tools worked well. We were able to edit our main spreadsheet and word documents dynamically. This allowed each member of the group to contribute content to the document simultaneously. Our schedules did not permit us to meet often but we used the discussion board as our main tool to communicate and update each other on the project. We also made use of other blackboard tools such as the file exchange. However, we had more success using the Google collaborative tools. Key Discussion Points and Lessons Learned Selection of broader and narrow terms For a number of photos, both broader and narrow terms were selected by the team members, for example, "rifles" and "weapons"; "men", "children", "women" and "people"; "sailboats", "steam boats", "yachts" and "transportation vehicles"; "kimonos" and "clothing and dress". After a discussion whether we should use broader terms, narrower terms or both, we decided to seek guidance from the instructor. The instructor's recommendation was to assign the most specific term available. This way, the search engine will be able to utilize the hierarchical structure of the thesaurus to auto-expand a query to include broader terms in it. Using both parent and child terms is not feasible due to the storage space consideration. Based on the instructor s recommendations, we selected the most specific indexing terms for most of the photos. One exception was a photo where it was not clear to us what kind of people were depicted on it, therefore, we decided to keep a broad term "people" instead of "men" and "children". Indexing of photos of religious objects For some of the pictures of Indian religious architecture objects that depicted statues of a deity, two of the team members felt that they don't have sufficient knowledge of Buddhism or Hinduism to determine which terms to use to describe the deity. Because the third team member, however, felt confident

7 that the statues did depict Buddha we selected the terms "Buddha" and "Buddhist Gods" for the image. Level of indexing detail Some discussion was around whether we should index some of the peripheral object on the images, e.g. a fan that a lawyer is holding or a clock and hanging scrolls inside a Tokyo temple. We concurred that we'll index the objects that occupy somewhat prominent position on the photos. Use of AAT preferred vs non-preferred terms While looking in AAT for an indexing term for an elephant from a photo of elephant riding in Thailand, we realized that Elephantidae (family) was a preferred indexing term in AAT. One of us searched AAT for other animals (e.g. tigers) and found that AAT uses zoological rather than colloquial terms for the animal terms found. This was rather surprising because AAT is a thesaurus used for indexing arts and architecture content. It's hard to imagine that someone searching for an elephant in an arts database would use elephant's scientific name. Therefore, we decided to use non-preferred term "elephants (animals)". Summary The most prominent lesson learned from this assignment is that indexing images is not as easy as it sounds. There are many terms that can be assigned to an image. However, one has to choose basic, indicative terms and be attentive to all the objects within that image. While this approach seems straightforward, it s rather complicated. This is because we associate words with a multitude of other words depending on the context according to Aitchison. Specifically, Aitchison states that people almost always select items from the semantic field of the original word. (Aitchison p. 73) This contributes to the difficulty of choosing terms. There is no guarantee that the user will choose the term that the indexer used. However, common sense would say there are certain basic objects that are easily discernable. It is the context in which the object is used that poses the dilemma. Appendix References Aitchison J. Slippery Customers (Chapter 4). Words in the Mind, An Introduction to the Mental Lexicon. Blackwell Publishing; Aitchison J. The Primordial Atomic Globule Hunt (Chapter 7). Words in the Mind, An Introduction to the Mental Lexicon. Blackwell Publishing; 1987

8 Hollink et al. Classification of user image descriptions. International Journal of Human-Computer Studies. 2004; 61(5): Jorgensen, C. Chapter 3: Organizing and Providing Access to Images. Image Retrieval. Latham, MS: Scarecrow Press; 2003.