Many local governments in Korea provide online services for people to easily access the audio-visual archives of events occurring in the area. However, the current method of managing these archives of the local governments has several problems in terms of compatibility with other organizations and convenience for searching of the archives because of the lack of standard metadata and the low utilization of image information. To solve these problems, we propose the metadata design and machine learning-based automatic indexing technology for the efficient management of the image archives of local governments in Korea. Moreover, we design metadata items specialized for the image archives of local governments to improve the compatibility and include the elements that can represent the basic information and characteristics of images into the metadata items, enabling efficient management. In addition, the text and objects in images, which include pieces of information that reflect events and categories, are automatically indexed based on the machine learning technology, enhancing users’ search convenience. Lastly, we developed the program that automatically extracts text and objects from image archives using the proposed method, and stores the extracted contents and basic information in the metadata items we designed.
