整合關鍵字與視覺特徵的反覆式影像檢索系統

Abstract

QBK是從人對於圖片的高階語意描述出發的一種圖片搜尋系統，其優點在於以人類的語意為基礎出發，並輔以成熟的文字檢索技術。QBK的缺點則在於圖片本身的內容對於檢索的影響可以說完全沒有，且圖片的文字描述並不能完全代表圖片本身所包含的內容。 CBIR則是從圖片本身的視覺特徵出發的一種圖片搜尋系統，其優點在於檢索結果完全依靠圖片本身的內容為主，完全客觀。其缺點目前CBIR的基礎技術仍不夠成熟，無法完美的模擬人類的辨別能力。 本研究綜合QBK系統和CBIR系統的優點，整合視覺特徵與關鍵字檢索技術，提出一個較為接近人類語義且以影像內容為基礎的圖片檢索系統。 本研究將QBK系統的查詢結果，透過CBIR中視覺特徵的擷取，將擷取出來的特徵值，再以資料探勘中的分群演算法加以分群，以區分出代表不同語意的影像。最後加上關鍵字擷取的技術，以關鍵字建議引導使用者作反覆式的搜尋，找到更貼近使用者語意的搜尋目標。

QBK is an image search approach based on text description. The advantage of QBK is that it is based on semantics of mankind, and assisted by the matured text-based search technology. However, the disadvantage of QBK is that the result of image search is not affected by the content of the image itself. Besides, the text description does not represent the content of image fully. CBIR is another image search approach which is based on the visual features of image itself. The advantage of CBIR is that the result of image search is all based on the content of the image and, it is objectively. The disadvantage of CBIR is that the basic technology is not matured enough. So the approach cannot imitate the recognition ability of human beings. Our approach is the combination of QBK and CBIR which integrates the advantage of visual features and text description. This approach not only access the semantics, but also base on the content of image. We extract the visual features with the method of CBIR from the result images of the QBK system. And then the images will be clustered by their visual features. Finally, users can iteratively search with keyword suggestions which are extracted from the description of clustered images.