RBIR using Interest Regions and Binary Signatures
This work addresses accuracy limitations in image retrieval systems, though it appears incremental as it builds on existing CBIR methods with region-based enhancements.
The paper tackles the low accuracy problem in Content-Based Image Retrieval (CBIR) by developing a Region-Based Image Retrieval (RBIR) approach using Harris-Laplace interest region detection and binary signatures for encoding, achieving improved accuracy on COREL's images.
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.