The query by image content (QBIC) system
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
VisualSEEk: a fully automated content-based image query system
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
A unified framework for semantics and feature based relevance feedback in image retrieval systems
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Modern Information Retrieval
Integrating Visual and Textual Cues for Image Classification
VISUAL '00 Proceedings of the 4th International Conference on Advances in Visual Information Systems
Spatial Color Indexing and Applications
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
Speed up interactive image retrieval
The VLDB Journal — The International Journal on Very Large Data Bases
An image retrieval system with automatic query modification
IEEE Transactions on Multimedia
Color image indexing using BTC
IEEE Transactions on Image Processing
A unified framework for image retrieval using keyword and visual features
IEEE Transactions on Image Processing
CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines
IEEE Transactions on Circuits and Systems for Video Technology
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This paper presents a joint querying and relevance feedback scheme based on both high-level and low-level features of images for an on-line content-based image retrieval system. In a high-level semantic retrieval system, 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. 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.