RBIR using Interest Regions and Binary Signatures
In this paper, we introduce an approach to overcome the low accuracy of the Content-Based Image Retrieval (CBIR) (when using the global features). To increase the accuracy, we use Harris-Laplace detector to identify the interest regions of image. Then, we build the Region-Based Image Retrieval (RBIR). For the efficient image storage and retrieval, we encode images into binary signatures. The binary signature of a image is created from its interest regions. Furthermore, this paper also provides an algorithm for image retrieval on S-tree by comparing the images' signatures on a metric similarly to EMD (earth mover's distance). Finally, we evaluate the created models on COREL's images.
PDF Abstract
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here
