Shot-based classification and retrieval is very important for video database organization and access. We present a new approach: 'nearest feature line - NFL' used in shot retrieval. We look at key-frames in a shot as feature points to represent the shot in feature space. Lines connecting the feature points are further used to approximate the variations in the whole shot. The similarity between the query image and the shots in video database are measured by calculating the distance between the query image and the feature lines in feature space. To make it more suited to video data, we improved the original NFL method by adding constraints on the feature lines. Experimental results show that our improved NFL method is better than the traditional classification methods such as nearest neighbor (NN) and nearest center (NC).
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