A new model for semantic photograph description combining basic levels and user-assigned descriptors

Few studies have been conducted to identify users’ desired semantic levels of image access when describing, searching, and retrieving photographs online. The basic level, or the level of abstraction most commonly used to describe an item, is a cognitive theory currently under consideration in image retrieval research. This study investigates potential basic levels of description for online photographs by testing the Hierarchy for Online Photograph Representation (HOPR) model, which is based on a need for a model that addresses users’ basic levels of photograph description and retrieval. We developed the HOPR model using the following three elements as guides: the most popular tags of all time on Flickr, the Pyramid model for visual content description by Jörgensen, Jaimes, Benitez, and Chang, and the nine classes of image content put forth by Burford, Briggs, and Eakins. In an exploratory test of the HOPR model, participants were asked to describe their first reaction to, and possible free-text indexing terms for, a small set of personal photographs. Content analysis of the data indicated a clear set of user preferences that are consistent with prior image description studies. Generally speaking, objects in the photograph and events taking place in the photograph were the most commonly used levels of description. The preliminary HOPR model shows promise for its intended utility, but further refinement is needed through additional research.

[1]  Brian C. O'Connor,et al.  Modelling what users see when they look at images: a cognitive viewpoint , 2002, J. Documentation.

[2]  Susanne Ornager Image Retrieval: Theoretical Analysis and Empirical User Studies on Accessing Information in Images. , 1997 .

[3]  Sara Shatford,et al.  Analyzing the Subject of a Picture: A Theoretical Approach , 1986 .

[4]  Stephen M. Kosslyn,et al.  Pictures and names: Making the connection , 1984, Cognitive Psychology.

[5]  Hemalata Iyer,et al.  Theories of cognition and image categorization: What category labels reveal about basic level theory , 2008, J. Assoc. Inf. Sci. Technol..

[6]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[7]  Diane Neal,et al.  News Photographers, Librarians, Tags, and Controlled Vocabularies: Balancing the Forces , 2008 .

[8]  Mari Laine-Hernandez,et al.  Image semantics in the description and categorization of journalistic photographs , 2007, ASIST.

[9]  JungWon Yoon,et al.  Towards a user-oriented thesaurus for non-domain-specific image collections , 2009, Inf. Process. Manag..

[10]  E. Panofsky Meaning in the Visual Arts , 1970 .

[11]  Peter G. B. Enser,et al.  Visual image retrieval , 2008, Annu. Rev. Inf. Sci. Technol..

[12]  Besiki Stvilia,et al.  User-generated collection-level metadata in an online photo-sharing system , 2009 .

[13]  Wayne D. Gray,et al.  Basic objects in natural categories , 1976, Cognitive Psychology.

[14]  Edmund A. Mennis The Wisdom of Crowds: Why the Many Are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations , 2006 .

[15]  Peter G. B. Enser,et al.  The evolution of visual information retrieval , 2008, J. Inf. Sci..

[16]  Brian C. O'Connor Structures of Image Collections: From Chauvet-Pont-d'Arc to Flickr , 2007 .

[17]  Jennifer Trant,et al.  Exploring the potential for social tagging and folksonomy in art museums: Proof of concept , 2006, New Rev. Hypermedia Multim..

[18]  Amanda Spink,et al.  Image searching on the Excite Web search engine , 2001, Inf. Process. Manag..

[19]  James Surowiecki The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies, and nations Doubleday Books. , 2004 .

[20]  Jonathon S. Hare,et al.  Facing the reality of semantic image retrieval , 2007, J. Documentation.

[21]  Shih-Fu Chang,et al.  Conceptual framework for indexing visual information at multiple levels , 1999, Electronic Imaging.

[22]  Journal of Information Science , 1984 .

[23]  Peter G. B. Enser,et al.  Progress in Documentation Pictorial Information Retrieval , 1995, J. Documentation.

[24]  Samantha K. Hastings,et al.  Free sorting of images: Attributes used for categorization , 2004, ASIST.

[25]  Diane Neal,et al.  Toward Web 2.0 music information retrieval: Utilizing emotion-based, user-assigned descriptors , 2007, ASIST.

[26]  Pamela Briggs,et al.  A Taxonomy of the Image: On the Classification of Content for Image Retrieval , 2003 .

[27]  Wolfgang G. Stock,et al.  Collective indexing of emotions in images. A study in emotional information retrieval , 2009, J. Assoc. Inf. Sci. Technol..

[28]  Bella Hass Weinberg Explorations in indexing and abstracting: Pointing, virtue, and power , 1997 .

[29]  M. Krause Intellectual problems of indexing picture collections , 1988 .

[30]  Howard Besser,et al.  Visual Access to Visual Images: The UC Berkeley Image Database Project , 1990 .

[31]  Bernard J. Jansen Searching for digital images on the web , 2008, J. Documentation.

[32]  Yong Man Ro,et al.  Semantic categorization of digital home photo using photographic region templates , 2007, Inf. Process. Manag..

[33]  K. Markey Interindexer consistency tests: a literature review and report of a test of consistency in indexing visual materials , 1984 .

[34]  Mark E. Rorvig,et al.  The NASA Image Collection Visual Thesaurus , 1999, J. Am. Soc. Inf. Sci..

[35]  Brian C. O'Connor Explorations in Indexing and Abstracting: Pointing, Virtue, and Power , 1996 .

[36]  Chih-Fong Tsai,et al.  A review of image retrieval methods for digital cultural heritage resources , 2007, Online Inf. Rev..

[37]  Marcia J. Bates,et al.  Indexing and Access for Digital Libraries and the Internet: Human, Database, and Domain Factors , 1998, J. Am. Soc. Inf. Sci..

[38]  R. Haber,et al.  Perception and memory for pictures: Single-trial learning of 2500 visual stimuli , 1970 .

[39]  Marc Davis,et al.  Photo annotation on a camera phone , 2004, CHI EA '04.

[40]  Peter G. B. Enser,et al.  Visual image retrieval: seeking the alliance of concept-based and content-based paradigms , 2000, J. Inf. Sci..

[41]  Abebe Rorissa,et al.  User-generated descriptions of individual images versus labels of groups of images: A comparison using basic level theory , 2008, Inf. Process. Manag..

[42]  Diane Neal North,et al.  Musical facets , tags , and emotion : Can we agree ? , 2009 .

[43]  Pauline Rafferty,et al.  Constructing an image indexing template for The Children's Society: Users' queries and archivists' practice , 2007, J. Documentation.

[44]  Shih-Fu Chang,et al.  A conceptual framework and empirical research for classifying visual descriptors , 2001, J. Assoc. Inf. Sci. Technol..

[45]  Corinne Jörgensen,et al.  The Visual Thesaurus in a Hypermedia Environment: A Preliminary Exploration of Conceptual Issues and Applications , 1991, ICHIM.

[46]  Krystyna K. Matusiak Information Seeking Behavior in Digital Image Collections: A Cognitive Approach , 2006 .

[47]  Diane Neal News photography image retrieval practices: Locus of control in two contexts. , 2006 .