相關性回饋 (Relevance Feedback) 和區域基礎影像擷取 (Region-Based Image Retrieval) 是二種被廣泛應用在增進以內容為主的影像擷取 (Content-Based Image Retrieval) 效能的方法。在此篇論文中，將這二種方法做結合，取代現有只將所有正相關性回饋 (Positive Feedback) 視為一堆的做法，在此提出了一個新的方法，試著採用區域基礎影像擷取加入分成多堆正負的相關性回饋。而且為了幫助使用者對正相關性回饋做分堆的動作，系統提供了引導式的預先分堆結果，使用者可再就這些預先分堆結果進行重新分堆的動作。接著，依人類視覺觀點，提出了一個估計區域重要性的方法。最後，再將正負相關性回饋丟入偏群式鑑別式分析 (Group-Biased Discriminant Analysis)，重新計算新的特徵空間轉換矩陣。

　Relevance feedback (RF) and region-based image retrieval (RBIR) are two widely used methods to enhance the performance of content-based image retrieval (CBIR) systems. In this paper, these two methods are combined. Rather than using only one positive feedback group, the proposed approach embeds the RF in RBIR with multiple positive and negative groups. In order to objectively assist the user in grouping the positive feedbacks, a heuristic pre-clustering result is automatically provided. Using these guiding clusters, the user can easily and subjectively re-group the feedbacks to express his/her particular interest. Furthermore, a region weighting scheme fitting the human visual perception is proposed to enhance the weighting importance of the region whose pixels are closer to the attention center. Finally, Group Biased Discriminant Analysis (GBDA) is modified and applied to the similarity measure between images based on these region-based relevance feedbacks.

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