## 4 Experimental Results and Analyses
### Experiment on Image-Query-Based Video Searching
#### 4.3.3 Experiment on Our Video Searching and Fingerprint Detection Dataset
Both the video searching accuracies and shot searching accuracies of SURF have dropped at least 10%. The highest video searching accuracy is less than 0.85 and the highest shot searching accuracy is less than 0.7. Consequently, although the SURF descriptor is faster, the SIFT descriptor should be adopted to achieve better performance. In conclusion, considering the accuracy and computing time, the control group is the better one among them for video searching and shot searching. The two thresholds also have a big effect on the final performance. When the threshold1 value is lower, the threshold2 value should be higher accordingly. As shown in Figures 16-19, the best thresholds for shot searching are different from the best thresholds for video searching. Additionally, the best thresholds also depend on the feature descriptors and video types. When using the SIFT descriptor, under the threshold1 value of 1.6 and the threshold2 value of 8 or 9, the video searching and shot searching accuracies are higher than under most other conditions. Figure 19: _Cont._  
Figure 18: The experimental results using the three frames method. (**a**) Video searching; (**b**) Shot searching. Results of Image-Query-Based Video Searching Experiments with Transformations  
From the above experiments, the threshold1s less than 1.6 have bad performance. Additionally, when the threshold1 value is larger than 3.0, the plot lines begin to go down. Considering image distortion situations, higher thresholds may not return any matches. However, in the experiments, the threshold range should be large enough. Therefore, we set threshold1 with a step size of 0.4 and decide to enlarge threshold2 from 2 to 16. During the shot feature generation stage, the frames are resized to 128 \(\times\) 128 and the step skip frames method is used. Transformations, such as flip, contrast change, noise addition, brightness change, cropping, rotation, and geometric projection, are commonly used for video copy detection. In addition, a picture in a picture, a fusion of pictures, and a pattern insertion are also used. To show the experimental results under transformations, we select five representative transformations: AT1, AT2, AT4, AT7, and AT9. AT1 employs flip. AT4 applies contrast change and noise addition. AT4 applies brightness change and cropping. AT7 uses geometric projection. AT9 was transformed using a picture in a picture and a pattern insertion. Figure 20 shows the results of video searching by using one query image under these transformations. Figure 17.0 The experimental results by using the SURF method. (**a**) Video searching; (**b**) Shot searching. Figure 17.1 _Cont._  
As seen in Figure 20, the different kinds of attacks lead to different results. Compared to Figure 16a, the accuracies of the flip transform and the geometric projection transform have dropped severely. The results of AT1 and AT7 are decreased by nearly 0.1 and 0.2, respectively. The attacks noise addition, contrast change, picture in a picture, and pattern insertion have not changed the image seriously as the video searching accuracies have dropped by no more than 0.06. In addition, the performances of the cropping and bright change transformations have decreased slightly. However, for query-image-based shot location detection, the accuracies have all dropped very seriously. Without transformations, the shot searching accuracy can reach 0.82. Under the transformations, the accuracies have dropped by at least 0.1. As shown in Figure 21, the results of shot searching are much lower than corresponding results for video searching. Compared to Figure 16b, the results of AT1 and AT4 have dropped by 0.17, and AT7 has decreased by 0.3. Even AT2 has dropped by 0.1. Although the shot searching results are lower, the video searching results under several transformations can be accepted for video searching and video copy detection. Figure 17.2 _Cont._  
Figure 17.3 Examples of video searching by using one image query under the transformations.