Applied multivariate statistical analysis
Applied multivariate statistical analysis
On “shapes” of colors for content-based image retrieval
MULTIMEDIA '00 Proceedings of the 2000 ACM workshops on Multimedia
Extraction of feature subspaces for content-based retrieval using relevance feedback
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
An effective region-based image retrieval framework
Proceedings of the tenth ACM international conference on Multimedia
MindReader: Querying Databases Through Multiple Examples
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
An affinity-based image retrieval system for multimedia authoring and presentation
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
A probabilistic framework for semantic video indexing, filtering,and retrieval
IEEE Transactions on Multimedia
A hierarchical approach to color image segmentation using homogeneity
IEEE Transactions on Image Processing
Affinity relation discovery in image database clustering and content-based retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
A unified framework for image database clustering and content-based retrieval
Proceedings of the 2nd ACM international workshop on Multimedia databases
Usage derived recommendations for a video digital library
Journal of Network and Computer Applications
Live television in a digital library
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
Rule-Based Semantic Concept Classification from Large-Scale Video Collections
International Journal of Multimedia Data Engineering & Management
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Recent research effort in Content-Based Image Retrieval (CBIR) focuses on bridging the gap between low-level features and high-level semantic contents of images as this gap has become the bottleneck of CBIR. In this paper, an effective image database retrieval framework using a new mechanism called the Markov Model Mediator (MMM) is presented to meet this demand by taking into consideration not only the low-level image features, but also the high-level concepts learned from the history of user's access pattern and access frequencies on the images in the database. Also, the proposed framework is efficient in two aspects: 1) Overhead for real-time training is avoided in the image retrieval process because the high-level concepts of images are captured in the off-line training process. 2) Before the exact similarity matching process, Principal Component Analysis (PCA) is applied to reduce the image search space. A training subsystem for this framework is implemented and integrated into our system. The experimental results demonstrate that the MMM mechanism can effectively assist in retrieving more accurate results from image databases.