Dynamic Feature Selection and Coarse-To-Fine Search for Content-Based Image Retrieval

We present a new approach to content-based image retrieval by addressing three primary issues: image indexing, similarity measure, and search methods. The proposed algorithms include: an image data warehousing structure for dynamic image indexing; a statistically based feature selection procedure to form flexible similarity measures in terms of the dominant image features; and a feature component code to facilitate query processing and guide the search for the best matching. The experimental results demonstrate the feasibility and effectiveness of the proposed method.