An ontology approach to object-based image retrieval

In this paper, an image retrieval methodology suited for search in large collections of heterogeneous images is presented. The proposed approach employs a fully unsupervised segmentation algorithm to divide images into regions. Low-level features describing the color, position, size and shape of the resulting regions are extracted and are automatically mapped to appropriate intermediatelevel descriptors forming a simple vocabulary termed object ontology. The object ontology is used to allow the qualitative definition of the high-level concepts the user queries for (semantic objects, each represented by a keyword) in a human-centered fashion. When querying, clearly irrelevant image regions are rejected using the intermediate-level descriptors; following that, a relevance feedback mechanism employing the low-level features is invoked to produce the final query results. The proposed approach bridges the gap between keyword-based approaches, which assume the existence of rich image captio...

• Type: Conference
• Year: 2003
• Where: ICIP
• Authors: Vasileios Mezaris, Ioannis Kompatsiaris, Michael G. Strintzis
• Keywords: ICIP 2003, Image Processing, Image Retrieval Methodology, Intermediate-level Descriptors, Irrelevant Image Regions, Object Ontology, Rich Image Captions