On-line Content-Based Image Retrieval System using Joint Querying and Relevance Feedback Scheme

In a high-level semantic retrieval process, we utilize the search engine to retrieve a large number of images using a given text-based query. In a low-level image retrieval process, the system provides a similar image search function for the user to update the input query for image similarity characterization. This paper presents an On-line Content-Based Image Retrieval System using joint querying and relevance feedback scheme based on both high-level and low-level features. We also introduce fast and efficient color feature extraction namely auto color correlogram and correlation (ACCC) based on color correlogram (CC) and autocorrelogram (AC) algorithms, for extracting and indexing low-level features of images. To incorporate an image analysis algorithm into the text-based image search engines without degrading their response time, the framework of multi-threaded processing is proposed. The experimental evaluations based on coverage ratio measure show that our scheme significantly improves the retrieval performance of existing image search engine. Key-Words: joint querying, image retrieval, ACCC, on-line CBIR, CBIR, relevance feedback