Applied Sstatistics - Principles and Examples by D. Cox, E. Snell

By D. Cox, E. Snell

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3. In order to surmount the obstacle, researchers improved the method in some way. In early years, the improvements were mainly centered on using adaptive thresholds [4]. At different parts of sequence, different thresholds are generated based on the continuity value of local frames. Recently, some contextual information based continuity computing methods are proposed [7]. The continuity of sequence is no longer based on the distance of only two adjacent frames in the feature space. The distances of all frame-pair in sequence context are used to detect shot boundary based on graph partition model in [7].

5(b). The face of the reference figure has many details similar to other faces in the figure. Therefore, this experiment evaluated the ability of the proposed method to find an object in a complex scene with rich color details. In this experiment, color Gaussian noise was added to the landscape image, in a percentage ranging from 5% to 75%. The objective of this test is to discover Particle Swarm Optimization for Object Recognition in Computer Vision (a) 19 (b) Fig. 5. (a) Landscape figure of a complex scene.

Therefore, we can tell the change at shot boundaries and the noises within shots apart with easy using refreshed subspace. The results of using refreshed and not-refreshed methods on frame 6420-6450 are compared in Fig. 9. 02 0 6420 6440 6460 6480 6500 6520 6540 6560 Frame Index Fig. 9. The comparison of methods using refreshed (the solid line) and non-refreshed (the dashed line) spaces. In non-refreshed space, basis v1 is generated from frame 6420-6450. The difference value only fluctuates obviously from frame 6480 to 6490 according to a rapid camera zooming and tracking.

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