Image retrieval by comparison between complete oriented graphs of fuzzy regions

Images comparison is the most critical step in the content-based image retrieval process. Therefore, we propose in this paper our approach of comparison based on image modeling by complete oriented graph. This structure encompasses low-level region descriptors in nodes and coarse spatial disposition in edges. Each node is characterized by its wavelet transformation high frequency sub-band weighted by the region importance. Similarity degree between two images is identified thereafter by comparing their graphs using heuristics to guarantee low computational overhead and to resolve the NP-hard matching problem between graphs. The experimental results and comparison made with similar image retrieval engines indicate the robustness of the proposed approach for Wang dataset and prove the applied heuristics.