Abstract-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 and endow the indexing and retrieval system with content-based functionalities. Low-level descriptors for the color, position, size and shape of each region are subsequently extracted. These arithmetic descriptors are automatically associated with appropriate qualitative intermediate-level descriptors, which form 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) and their relations in a human-centered fashion. When querying for a specific semantic object (or objects), the intermediate-level descriptor values associated with both the semantic object and all image regions in the collection are initially compared, resulting in the rejection of most image regions as irrelevant. Following that, a relevance feedback mechanism, based on support vector machines and using the low-level descriptors, is invoked to rank the remaining, potentially relevant image regions and produce the final query results. Experimental results and comparisons demonstrate in practice the effectiveness of our approach.
Mezaris et al. REF propose an approach that employs a fully unsupervised segmentation algorithm and associate low-level descriptors with appropriate qualitative intermediate-level descriptors, which form a simple vocabulary termed object ontology.
Region-based Image Retrieval using an Object Ontology and Relevance Feedback
ICIP (1)
Computer Science