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
Content based image retrieval and information theroy: a general approach
Journal of the American Society for Information Science and Technology - Visual based retrieval systems and web mining
Relevance feedback in content-based image retrieval: some recent advances
Information Sciences—Applications: An International Journal
Automatic image annotation and retrieval using cross-media relevance models
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A Learning-based Approach for Annotating Large On-Line Image Collection
MMM '04 Proceedings of the 10th International Multimedia Modelling Conference
An Overview of Content-based Image Retrieval Techniques
AINA '04 Proceedings of the 18th International Conference on Advanced Information Networking and Applications - Volume 2
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
User term feedback in interactive text-based image retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A Unified Log-Based Relevance Feedback Scheme for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
Information Sciences: an International Journal
Real-Time Computerized Annotation of Pictures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Relevance feedback: a power tool for interactive content-based image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
Scalable multi-feature index structure for music databases
Information Sciences: an International Journal
A unified methodology for the efficient computation of discrete orthogonal image moments
Information Sciences: an International Journal
A systematic method for efficient computation of full and subsets Zernike moments
Information Sciences: an International Journal
Integrating induction and deduction for noisy data mining
Information Sciences: an International Journal
A relevance feedback method based on genetic programming for classification of remote sensing images
Information Sciences: an International Journal
VisionGo: Towards video retrieval with joint exploration of human and computer
Information Sciences: an International Journal
Weighted Association Rule Mining for Video Semantic Detection
International Journal of Multimedia Data Engineering & Management
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An accurate and rapid method is required to retrieve the overwhelming majority of digital images. To date, image retrieval methods include content-based retrieval and keyword-based retrieval, the former utilizing visual features such as color and brightness, and the latter utilizing keywords that describe the image. However, the effectiveness of these methods in providing the exact images the user wants has been under scrutiny. Hence, many researchers have been working on relevance feedback, a process in which responses from the user are given as feedback during the retrieval session in order to define a user's need and provide an improved result. Methods that employ relevance feedback, however, do have drawbacks because several pieces of feedback are necessary to produce an appropriate result, and the feedback information cannot be reused. In this paper, a novel retrieval model is proposed, which annotates an image with keywords and modifies the confidence level of the keywords in response to the user's feedback. In the proposed model, not only the images that have been given feedback, but also other images with visual features similar to the features used to distinguish the positive images are subjected to confidence modification. This allows for modification of a large number of images with relatively little feedback, ultimately leading to faster and more accurate retrieval results. An experiment was performed to verify the effectiveness of the proposed model, and the result demonstrated a rapid increase in recall and precision using the same amount of feedback.