Sciweavers

Share
CVPR
2001
IEEE

Learning Similarity Measure for Natural Image Retrieval with Relevance Feedback

9 years 10 months ago
Learning Similarity Measure for Natural Image Retrieval with Relevance Feedback
A new scheme of learning similarity measure is proposed for content-based image retrieval (CBIR). It learns a boundary that separates the images in the database into two parts. Images on the positive side of the boundary are ranked by their Euclidean distances to the query. The scheme is called restricted similarity measure (RSM), which not only takes into consideration the perceptual similarity between images, but also significantly improves the retrieval performance based on the Euclidean distance measure. Two techniques, support vector machine and AdaBoost, are utilized to learn the boundary, and compared with respect to their performance in boundary learning. The positive and negative examples used to learn the boundary are provided by the user with relevance feedback. The RSM metric is evaluated on a large database of 10,009 natural images with an accurate ground truth. Experimental results demonstrate the usefulness and effectiveness of the proposed similarity measure for image ...
Guodong Guo, Anil K. Jain, Wei-Ying Ma, HongJiang
Added 12 Oct 2009
Updated 29 Oct 2009
Type Conference
Year 2001
Where CVPR
Authors Guodong Guo, Anil K. Jain, Wei-Ying Ma, HongJiang Zhang
Comments (0)
books