Content Based Image Retrieval System Using NOHIS-tree
This work addresses the challenge of efficiently searching large image databases for computer vision applications, but it appears incremental as it builds on existing indexing methods.
The paper tackles the problem of content-based image retrieval (CBIR) by proposing a system called NOHIS-Search based on the NOHIS-tree indexing technique, and it shows that this system outperforms two other systems (PDDP indexing and sequential search) in performance tests on the ImagEval database.
Content-based image retrieval (CBIR) has been one of the most important research areas in computer vision. It is a widely used method for searching images in huge databases. In this paper we present a CBIR system called NOHIS-Search. The system is based on the indexing technique NOHIS-tree. The two phases of the system are described and the performance of the system is illustrated with the image database ImagEval. NOHIS-Search system was compared to other two CBIR systems; the first that using PDDP indexing algorithm and the second system is that using the sequential search. Results show that NOHIS-Search system outperforms the two other systems.