抄録
An approach is described for classifying images that does not require first dividing an image into small blocks and then classifying it based on the features of the individual blocks, as do traditional approaches. Instead the line features of the entire image are extracted and used, along with the pixel intensity, to classify each pixel in the image. To increase the classification speed, a fast line-extraction algorithm has been developed that extracts the line features directly from the original image without pre-processing. A classification tree with single variable splits is used to classify the image. Testing of a five-class aerial-image classification algorithm showed that it had an average error rate of 17.6%. Running on a 600-MHz Pentium III processor, it had an average classification time of 2.18 seconds for 512×512 grayscale images. This approach can be used for many different applications by training the classification tree with the desired classes.
