Region-Based Image Retrieval Using an Object Ontology and Relevance Feedback

Region-Based Image Retrieval Using an Object Ontology and Relevance Feedback

Blog Article

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 strikketøy oppbevaring 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 lolasalinas.com 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.