| Summary: | 碩士 === 國立政治大學 === 圖書資訊與檔案學研究所 === 107 === “Digital image”, in the information development era, has become an important data pattern supporting research on digital humanities. The development also creates new challenge and development opportunities for humanities studies in the digital time. Past research indicated that the display of digital images should not be a long list or a thumbnail, but should exist in the object message which could immediately absorb information visually and allow readers presenting better image organization ability. For this reason, it becomes more important to largely recognize and analyze objects existing in digital images. “Image annotation” plays an inevitably role; digital images are described by determining proper vocabulary to reduce the semantic gap between human users’ image explanation and image low-level features. Along with the development of technology, “automatic image annotation”, based on image annotation, could reduce the cost for manual annotation and present advantages of high efficiency and low subjectivity. It facilitates the research on exploring humanists’ use difference and perception of individual image interpretation with the assistance of “automatic image annotation”. This study attempts to understand how and why users use images from the aspect of humanists and further develop an effective digital humanities tool assisting humanists in image situation interpretation.
“Image retrieval tool based on automatic image annotation (IRT-AIA)” is therefore developed in this study. The core technology of the system is to apply Mask R-CNN, the algorithm to implement instance segmentation tasks in image recognition, to recognize physical objects in images. In addition to specifically recognize the categories and locations of independent physical object in images, it would further draw the profile of various physical objects to rapidly extract the physical object message in digital images. Such message is presented as the alternative information of image set, allowing users rapidly absorbing and effectively organizing image contents. Finally, a friendly interface to enhance the interaction between humanists and the system allows humanists preceding image annotation under individual interpretation to rapidly acquire the meta-data content of digital images and further facilitate more efficient image situation interpretation of humanists.
To verify IRT-AIA developed in this study being able to assist humanists in image interpretation, counterbalanced design in quasi-experimental research is applied in this study. The users are divided into two groups to complete tasks at different stages by operating IRT-AIA and general image retrieval tool (GIRT), according to different system use sequence. Behavior process recording is also utilized for completely recording users’ system operation behaviors, technology acceptance model questionnaire is applied to reflect users’ actual perception, and semi-structured in-depth interview is used for understanding user’s ideas and suggestions. With the mutual verification through various methods, the differences in automatic image annotation accuracy, image retrieval accuracy in image situation interpretation, image situation interpretation effectiveness, and technology acceptance between IRT-AIA and GIRT developed in this study are understood.
The research results are summarized as followings. First, the automatic image annotation accuracy of IRT-AIA could effectively assist users in interpreting image situation. Second, the use of IRT-AIA could acquire better image retrieval precision rate and good recall rate. Third, the image situation interpretation effectiveness between the use of GIRT and IRT-AIA does not achieve significant differences. The analyses reveal that community tag and artificial intelligence tag present distinct purposes that a system laying equal stress on both could satisfy users’ needs for different retrieval. Fourth, the use of GIRT and IRT-AIA does not reach remarkable differences in technology acceptance, but presents good technology acceptance above the median. Fifth, users, in the process of using community tag and artificial intelligence tag for browse and retrieval, prefer artificial intelligence tag, which allows users more easily acquiring the target image.
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