台灣留學生出席國際會議補助

2010年8月11日 星期三

Classification of Raster Maps for Automatic Feature Extraction

論文發表人:蔣耀毅(南加州大學資訊科學系博士班)

 

http://acmgis09.cs.umn.edu/papers.html

 

要擷取點陣地圖裡的地理資訊,第一步通常是需要人工辨認 (manual training) 地圖裡的前景,而這樣的方法使得資訊擷取的系統無法全盤自動化。在這一篇文章裡,我們設計了一個地圖分類的系統,使用Luminance-Boundary Histogram以及一個Nearest-Neighbor Classifier來分類每一張新進的地圖,進而自動找出與新進地圖相似並且已經經過人工辨認的地圖。因此,我們可以重複使用人工辨認的結果進而達到資訊擷取完全自動化的系統。

 

Raster maps are widely available and contain useful geographic features such as labels and road lines. To extract the geographic features, most research work relies on a manual step to first extract the foreground pixels from the maps using the distinctive colors or grayscale intensities of the pixels. This strategy requires user interaction for each map to select a set of thresholds. In this paper, we present a map classification technique that uses an image comparison feature called the luminance-boundary histogram and a nearest-neighbor classifier to identify raster maps with similar grayscale intensity usage. We can then apply previously learned thresholds to separate the foreground pixels from the raster maps that are classified in the same group instead of manually examining each map. We show that the luminance-boundary histogram achieves 95% accuracy in our map classification experiment compared to 13.33%, 86.67%, and 88.33% using three traditional image comparison features. The accurate map classification results make it possible to extract geographic features from previously unseen raster maps.