The Community for Technology Leaders
2013 IEEE 29th International Conference on Data Engineering (ICDE) (2006)
Atlanta, Georgia
Apr. 3, 2006 to Apr. 7, 2006
ISBN: 0-7695-2570-9
pp: 84
Ning Yu , University of Central Florida
Danzhou Liu , University of Central Florida
Kien A. Hua , University of Central Florida
Today?s Content-Based Image Retrieval (CBIR) techniques are based on the "k-nearest neighbors" (k- NN) model. They retrieve images from a single neighborhood using low-level visual features. In this model, semantically similar images are assumed to be clustered in the high-dimensional feature space. Unfortunately, no visual-based feature vector is sufficient to facilitate perfect semantic clustering; and semantically similar images with different appearances are always clustered into distinct neighborhoods in the feature space. Confinement of the search results to a single neighborhood is an inherent limitation of the k-NN techniques. In this paper we consider a new image retrieval paradigm — the Query Decomposition model - that facilitates retrieval of semantically similar images from multiple neighborhoods in the feature space. The retrieval results are the k most similar images from different relevant clusters. We introduce a prototype, and present experimental results to illustrate the effectiveness and efficiency of this new approach to content-based image retrieval.
Ning Yu, Danzhou Liu, Kien A. Hua, "Query Decomposition: A Multiple Neighborhood Approach to Relevance Feedback Processing in Content-based Image Retrieval", 2013 IEEE 29th International Conference on Data Engineering (ICDE), vol. 00, no. , pp. 84, 2006, doi:10.1109/ICDE.2006.123
118 ms
(Ver 3.1 (10032016))