我們使用一種記錄輪廓特質的方法，來當我們的形狀描述子。這個方法是將輪廓用n個不連續的點表示，對於每一個參考點，記錄剩下n – 1個點和參考點的相對位置。我們可以發現，當輪廓被旋轉時，這樣的記錄結果也會被旋轉，因此，如果這些結果彼此有旋轉關係的話，我們就把他群聚在一起，並且用一個來符號表示。所以，本來輪廓是由n個點來表示的，現在變成用n個符號來表示。對於每個輪廓，統計這些符號出現的次數後再與資料庫中的圖片做比對，就可以快速地找到輪廓相似，或是輪廓經由旋轉過後相似的圖。
我們把這種輪廓比對的技巧，套用在一個現有的影像擷取系統中，這個系統是同時具有相關性回饋，以及區域基礎的影像擷取方法，是一個符合人類思考模式的系統。如此一來，對於影像擷取的結果將會有很高的準確性。

In this work we use shape context as our shape descriptor. The representation for a shape is a discrete set of n points. For each of these points, the shape context is a histogram of the relative positions of the remaining points. When a shape is rotated, the shape context is rotated too. We group the rotated shape contexts together and then label each group by an integer. Therefore, a shape is represented by a set of label. Using the histogram of label frequencies can quickly and efficiently search for similar or rotational shapes.
We use this shape retrieval method to integrate with an existent retrieval system which utilizes relevance feedback in region-based image retrieval. The system will learn the user’s semantic subjectivity. Hence, well accuracy is demonstrated in the results of image retrieval.

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