Fast Query Point Movement Techniques for Large CBIR Systems

Abstract

Abstract-Target search in content-based image retrieval (CBIR) systems refers to finding a specific (target) image such as a particular registered logo or a specific historical photograph. Existing techniques, designed around query refinement based on relevance feedback, suffer from slow convergence, and do not guarantee to find intended targets. To address these limitations, we propose several efficient query point movement methods. We prove that our approach is able to reach any given target image with fewer iterations in the worst and average cases. We propose a new index structure and query processing technique to improve retrieval effectiveness and efficiency. We also consider strategies to minimize the effects of users' inaccurate relevance feedback. Extensive experiments in simulated and realistic environments show that our approach significantly reduces the number of required iterations and improves overall retrieval performance. The experimental results also confirm that our approach can always retrieve intended targets even with poor selection of initial query points.Index Terms-Content-based image retrieval, relevance feedback, target search, index structures.

Clusters-Based Relevance Feedback for CBIR: A Combination of Query Movement and Query Expansion

“…In [12], authors confirmed that three-example queries are more effective than other numbers of examples; we will evaluate our method with different values of c.…”

“…The number of clusters is limited to a fixed number by using a clustermerging method. But this complex approach is unable to make effective use of the irrelevant examplesIn [12], authors propose a fast query point movement technique to adapt the target-search in CBIR. All above methods still have drawbacks including local maximum traps and slow convergence.…”