傳統的影像內容檢索方法大多以計算使用者查詢圖片與系統中所有圖片之間的相似度為基礎，此稱為「範例式查詢系統」(Query-By-Example System)。然而，在現今既存的查詢回饋方法中，使用短期限的查詢回饋是很難改進圖片搜尋準確度。因此，這引發了我們的動機去發展一個重新定義的查詢圖片的技術 (Query Refinement Technique)，稱其NQ3。NQ3方法結合了瀏覽樣式(Navigation Patterns) 與混合型搜尋(Hybrid Search)的方法。混合型搜尋方法，我們稱之Q3， Q3代表的是「移動查詢中心點」(Query-Point-Movement, QPM)，「查詢特徵權重值調整」(Query-Reweighting, QR )，與「擴充查詢」(Query-Expansion, QEX) 三種方法的結合。普遍說來我們的方法與現今其他的方法最主要的不同在於我們的方法可以達到較高的準確率，且同時將視覺化多樣性、搜尋收斂、冗餘瀏覽的問題考量進去。因此，NQ3 可以搜尋有效性與效率地來進行互動式影像內容擷取。就效率性而言，藉由探勘使用者瀏覽圖片樣式的規則輔助，避免冗長的瀏覽回饋的次數；就有效性而言，藉由使用三種查詢關聯性回饋技術去克服圖片視覺上特徵值差異性的問題；就準確性跟回饋次數來看，我們的實驗結果證實我們的方法優於目前存在的方法。

Conventional approaches for content-based image retrieval are always on the basis of the computations of the similarities between user’s query and images via a query-by-example system. Indeed, no matter how the search strategies are powerful, it is very hard to make a precise search within a very short term of query feedbacks. Hence, it motivated us to develop an innovative query refinement technique, namely NQ3, that combines navigation patterns and the hybrid search strategy, namely Q3, with respect to QPM (Query-Point-Movement), QR (Query-Reweighting) and QEX (Query-EXpansion). Generally, the primary difference between our proposed approach and the other contemporary approaches is that we have accomplished excellent image retrieval with considering the problems for visual diversity, exploration convergence, redundant browse. In other words, the major contribution of this paper is that we propose a new Query-Refinement technique to help user make an effective and efficient exploration of images. For efficiency, the expected query formulation scenario is that the intolerant navigations are expected to be prevented by exploiting the navigation patterns hidden in user behaviors. For effect, three query refinement strategies will cooperate to overcome the obstacle about feature diversity. The experimental results reveal that our proposed approach is very effective for query refinement in terms of accuracy through the integration of navigation patterns and Q3.

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