Important Note: This article is part of the series in which TechReport.us discuss theory of Video Stream Matching.
After all features calculating these features are send to classification model where these features are used for decision making. Basic theme for all models is same so here only discuss the abstract approach for decision making.
4.5.1 Algorithm 10
Input:
Any feature text file, one of them, wavelet, histogram, edge and slope. This is for source video and target video or target image.
Output:
Define if result is found or not.
Working:
Read text files of data. Data is already calculated features store in files. Now rearrange the data according to logic and then send to classifier model part 1.Part 1 is the part where minimum distance between data is found. These values are sends to part 2 of classifier where final decision is make. Decision is that if video frame match or not.
Step 0 : Start
Step 1 : Read files for define feature of VSM for Source (sf1)
Step 2 : If target is video
Then
Read files for define feature of VSM for Target Video(tf1)
else
Read files for define feature of VSM for Target Image(tf1)
Step 3 : (dt) = Arrange data for (tf1)
Step 4 : (ds) = Arrange data for (sf1)
Step 5 : numt = Size(dt)
Step 6 : nums = Size(ds) ; inc = 1;
Step 7 : Loop1: for I =1:1:numt // for each target image
Step 8 : nt = VAR(dt(I));
Step 9 : Loop2: for J = 1:1:nums // For every source image
Step 10 : ns = VAR(ds(J));
Step 11 :
Res = norm(nt,ns);
ResArray(inc) = Res
Inc = inc +1 ;
Step 12 : end Loop1
Inc = 1;
Step 13 : end Loop2
Step 14 : [m, loct, locs ] = min(ResArray)
Step 15 : Loop1: for I =1:1:numt // for each target image
Step 16 : imt = imread(loct(I))
Step 17 : ims = imread(locs(I))
Step 18 : h1 = hcal(imt);
Step 19 : h2 = hcal(ims);
Step 20 : a = KSTEST(h1,h2)
If a==0
OutPut “ image matches”
Else
OutPut “ image not matches”
End if
Step 21 : end Loop1
Step 22 : Stop