Abstract-This paper introduces a system for automatic annotation of videos based on knowledge extracted from a pre-annotated datasets. Constructing knowledge representations from a customised set of low-level features, the system generates inference rules linking low-level descriptors with user defined semantic concepts, whether they are keywords or any other symbol that conveys the video semantics to the user. The system utilises fuzzy logic and data mining to achieve human-like approximate reasoning.Using a rule-knowledge-base created in the learning process, keywords from a user defined lexicon are assigned to new video clips added to the database. Exploiting efficient low-level video representations, the system performance was assessed on a dataset containing over 100 broadcasting videos. The experimental evaluation showed robust and high annotation accuracy. The system architecture offers straightforward expansion to relevance feedback and autonomous learning capabilities.
Research in Content-Based Image Retrieval is an expanding discipline with an accelerated growing in the last ten years. Advances in telecommunications and the huge demand of visual information on Internet and mobile devices is occupying the attention of the researchers in developing efficient systems to ease the task of useful visual information retrieval by the users. This work presents a semi-automatic image annotation process using the low-level image descriptor Fuzzy Color Signature to extract the most similar images from an annotated database and frequent pattern mining to select the candidates keywords for annotating the new image. The idea is aimed at establishing a bridge between visual data and their interpretation using a weak semantic approach.
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