Representative subscene classification

Representative subscene classification refers to the process of selecting a representative subset of an image, or a "subscene," and using it to classify the entire image. This approach is used when the image data is too large or complex to be classified as a whole, and instead, a smaller representative subset of the image is used to classify the entire image.

The process of representative subscene classification typically begins with the selection of the representative subset, which is a small portion of the image that is representative of the entire image. This subset is then manually or automatically labeled with the appropriate class labels. Next, an algorithm, such as a decision tree or a support vector machine, is trained on the labeled subset, and then used to classify the entire image.

This approach is useful when the image data is too large or complex to be classified as a whole and also when it's too costly to manually label the entire image. By selecting a representative subset of the image, the classification process is simplified and the accuracy is increased.

Representative subscene classification can also be used in combination with other classification methods such as interactive preliminary classification or self-classification of training data set to improve the classification accuracy.

Overall, representative subscene classification is a useful method for image classification, as it allows for the classification of large or complex image data by using a smaller representative subset of the image, which is more manageable to classify and provides better accuracy.

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