Image database retrieval utilizing affinity relationships

Recent research effort in Content-Based Image Retrieval (CBIR) focuses on bridging the gap between low-level features and highlevel 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 ...

• Type: Conference
• Year: 2003
• Where: MMDB
• Authors: Mei-Ling Shyu, Shu-Ching Chen, Min Chen, Chengcui Zhang, Kanoksri Sarinnapakorn
• Keywords: Content-based Image Retrieval, Image, Image Retrieval, MMDB 2003