VIDEO SHOT BOUNDARY DETECTION(19)

BOUNDARY DETECTION(19)

Within one shot, due to the motion, appearance or disappearance of objects, the color histogram may not be consistent. Gist captures the overall texture of the background while ignoring these small change due to foreground objects. A classifier trained to detect such rare occurrence would be biased to treat all frames as non-transition. But the classifier approach is impractical. Instead, we treat the task of detecting shot boundaries as an outlier detection problem. We use a simple z-score metric to determine outliers. The first strategy measures the z-score across all dimensions (assuming they are independent). The second strategy counts the number of dimensions in which the given data-point is an outlier. Payday Loans Online

In practice, we observe that both strategies perform well. We define detected interval as the interval of the group of consecutive detected frames, and its width the number of frames it contains. For each detected interval, we mark it as cut if its width is higher than some threshold wt, as gradual transition otherwise.

This method proposed gist and color features for shot-boundary detection in video. This method posed the task as an outlier detection problem in the gradient domain and proposed a simple z-score based approach to solve the problem. We observe that gist is better than color features for detecting abrupt transitions. However, color performs better for gradual transitions.

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