Abstract: A method discovers patterns in unknown multi-dimensional data. A time-series of the multi-dimensional data is generated and a point cross-distance matrix is constructed by self-correlating the time-series. All minimum cost paths in the point cross-distance matrix are located at multiple time resolutions. The minimum cost paths are then related to temporal sub-sequences in the multi-dimensional data to discover high-level patterns in the unknown multi-dimensional data.

Abstract: A method extracts an intensity of motion activity from shots in a compressed video. The method then uses the intensity of motion activity to segment the video into easy and difficult segments to summarize. Easy to summarize segments are represented by any frames selected from the easy to summarize segments, while a color based summarization process extracts generates sequences of frames from each difficult to summarize segment. The selected and generated frames of each segment in each shot are combined to form the summary of the compressed video.

Abstract: A method describes motion activity in a video sequence. A motion activity matrix is determined for the video sequence. A threshold for the motion activity matrix is determined. Connected regions of motion vectors at least equal to the threshold are identified and measured for size. A histogram of the distribution of the sizes of the connected areas is constructed for the entire video sequence. The histogram is normalized to characterize the spatial distribution of the video sequence in a motion activity descriptor.

Abstract: A method for summarizing a video first detects audio peaks in a sub-sampled audio signal of the video. Then, motion activity in the video is extracted and filtered. The filtered motion activity is quantized to a continuous stream of digital pulses, one pulse for each frame. If the motion activity is greater than a predetermined threshold the pulse is one, otherwise the pulse is zero. Each quantized pulse is tested with respect to the timing of rising and falling edges. If the pulse meets the condition of the test, then the pulse is selected as a candidate pulse related to an interesting event in the video, otherwise the pulse is discarded. The candidate pulses are correlated, time-wise to the audio peaks, and patterns between the pulses and peaks are examined. The correlation patterns segment the video into uninteresting and interesting portions, which can then be summarized.

Abstract: A method measures an intensity of motion activity in a compressed video. The intensity of the motion activity is used to partition the video into segments of equal cumulative motion activity. Key-frames are then selected from each segments. The selected key-frames are concatenated in temporal order to form a summary of the video.