5 research outputs found
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A Common Representation Format for Multimedia Documents
Multimedia documents are composed of multiple file format combinations, such as image and text, image and sound, or image, text and sound. The type of multimedia document determines the form of analysis for knowledge architecture design and retrieval methods. Over the last few decades, theories of text analysis have been proposed and applied effectively. In recent years, theories of image and sound analysis have been proposed to work with text retrieval systems and progressed quickly due in part to rapid progress in computer processing speed. Retrieval of multimedia documents formerly was divided into the categories of image and text, and image and sound. While standard retrieval process begins from text only, methods are developing that allow the retrieval process to be accomplished simultaneously using text and image. Although image processing for feature extraction and text processing for term extractions are well understood, there are no prior methods that can combine these two features into a single data structure. This dissertation will introduce a common representation format for multimedia documents (CRFMD) composed of both images and text. For image and text analysis, two techniques are used: the Lorenz Information Measurement and the Word Code. A new process named Jeong's Transform is demonstrated for extraction of text and image features, combining the two previous measurements to form a single data structure. Finally, this single data measurements to form a single data structure. Finally, this single data structure is analyzed by using multi-dimensional scaling. This allows multimedia objects to be represented on a two-dimensional graph as vectors. The distance between vectors represents the magnitude of the difference between multimedia documents. This study shows that image classification on a given test set is dramatically improved when text features are encoded together with image features. This effect appears to hold true even when the available text is diffused and is not uniform with the image features. This retrieval system works by representing a multimedia document as a single data structure. CRFMD is applicable to other areas of multimedia document retrieval and processing, such as medical image retrieval, World Wide Web searching, and museum collection retrieval
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Image manipulation and user-supplied index terms.
This study investigates the relationships between the use of a zoom tool, the terms they supply to describe the image, and the type of image being viewed. Participants were assigned to two groups, one with access to the tool and one without, and were asked to supply terms to describe forty images, divided into four categories: landscape, portrait, news, and cityscape. The terms provided by participants were categorized according to models proposed in earlier image studies. Findings of the study suggest that there was not a significant difference in the number of terms supplied in relation to access to the tool, but a large variety in use of the tool was demonstrated by the participants. The study shows that there are differences in the level of meaning of the terms supplied in some of the models. The type of image being viewed was related to the number of zooms and relationships between the type of image and the number of terms supplied as well as their level of meaning in the various models from previous studies exist. The results of this study provide further insight into how people think about images and how the manipulation of those images may affect the terms they assign to describe images. The inclusion of these tools in search and retrieval scenarios may affect the outcome of the process and the more collection managers know about how people interact with images will improve their ability to provide access to the growing amount of pictorial information
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In Pursuit of Image: How We Think About Photographs We Seek
The user perspective of image search remains poorly understood. the purpose of this study is to identify and investigate the key issues relevant to a user’s interaction with images and the user’s approach to image search. a deeper understanding of these issues will serve to inform the design of image retrieval systems and in turn better serve the user. Previous research explores areas of information seeking behavior, representation in information science, query formulation, and image retrieval. the theoretical framework for this study includes an articulation of image search scenarios as adapted from Yoon and O’Connor’s taxonomy of image query types, Copeland’s Engineering Design Approach for rigorous qualitative research, and Anderson’s Functional Ontology Construction Model for building robust models of human behavior. a series of semi-structured interviews were conducted with expert-level image users. Interviewees discussed their motivations for image search, types of image searches they pursue, and varied approaches to image search, as well as how they decide that an information need has been met and which factors influence their experience of search. a content analysis revealed themes repeated across responses, including a collection of 23 emergent concepts and 6 emergent categories. a functional analysis revealed further insight into these themes. Results from both analyses may be used as a framework for future exploration of this topic. Implications are discussed and future research directions are indicated. Among possibilities for future research are investigations into collaborative search and ubiquitous image search