Image Retrieval Based on Binary Signature ang S-kGraph
This work addresses efficiency issues in large-scale image retrieval systems, though it appears incremental as it builds on existing RBIR methods.
The paper tackles the computational cost problem in region-based image retrieval by introducing a binary signature encoder and S-kGraph classification, achieving significant improvements in query accuracy on the COREL dataset.
In this paper, we introduce an optimum approach for querying similar images on large digital-image databases. Our work is based on RBIR (region-based image retrieval) method which uses multiple regions as the key to retrieval images. This method significantly improves the accuracy of queries. However, this also increases the cost of computing. To reduce this expensive computational cost, we implement binary signature encoder which maps an image to its identification in binary. In order to fasten the lookup, binary signatures of images are classified by the help of S-kGraph. Finally, our work is evaluated on COREL's images.