在視覺資訊的管理中，影像註解是將影像加上一組預定的關鍵字，可應用於各種領域方面，如網路影像分類、搜尋、軍事、生物科學…等。
　　邊緣/內部的像素分類特徵(BIC features)是非常有效且簡潔的特徵，它可以擷取顏色、圖形、以及紋理等資訊。但BIC特徵有些問題，即每個特徵的利用率相當的不平均。為了解決這個問題，我們使用了Hilbert-scan和一次分割的方法來改善。最後，我們的實驗是註解60個不同種類共6000張的影像於KNN以及SVMs中來比較其準確率。

　　For visual information management, image annotation which refers to the labeling of images with a set of predefined keywords is mainly used in a variety of domains such as web image classification, search, military, biomedicine, etc.
　　The Border/Interior pixel Classification (BIC) features [15] are very efficient and compact features that capture the information of color, shape, and texture. But the BIC features inherit the problem that the utilization rates are not balanced. To overcome this problem, we propose to employ the Hilbert-Scan method and the One-pass Partitioning Method (OPM). Finally, we show the accuracy by our proposed method with KNN and SVMs in annotating 6000 images in 60 categories.

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