以CIDOC CRM為基礎的推論平台–整合異質性影像資料庫的詮釋性與概念性知識

Abstract

Recently, advances in storage techniques have conducted to an increase in a lot amount of digital images all around the world. These developments have heightened the need for effective image retrieval techniques. Roughly speaking, image retrieval techniques can be divided into two areas: the traditional keyword-based approach and content-based image retrieval. But they still fail on low precision and lack semantic that will cause semantic gap between image features and the user. Previous studies have demonstrated that non-professional users prefer using event-based conceptual descriptions, such as “a woman wearing a hat”, to describe and search images. In many art image archives, these conceptual descriptions are manually annotated in free-text fields. We propose a novel approach for extracting event-based semantic knowledge automatically from free-text image descriptions. The semantic role labeling techniques appear to be a promising technology for transforming the unrestricted natural language texts into structured records that is easy to index/retrieve using relational database technologies. The precision of such SRL-assisted event-based image retrieval is much higher than that of the conventional keyword-based approach. This thesis proposes an ontology-based approach for integrating metadata-based image annotations (administrative knowledge) with event-based knowledge (administrative and conceptual knowledge), including subject, verb, object, location and temporal information from free-text image descriptions. The goal is to automatically derive certain interesting knowledge, such as, all the paintings with a same painting style and a same conceptual event in the image content that implicitly dispersed around different image archives. In practice, an answer to these questions requires a series of field-based queries, across different digital archives. This thesis investigated the possibility to utilize standardized ontology, CIDOC-CRM, and semantic web tools, such as Protege, SWRL and Jess inference engine in order to model an inference platform. We discuss the need for a robust inference platform for real-life knowledge discovery and integration among heterogeneous image archives. Experimental results indicate the approach can be implemented in real-life applications.