Heyy guys,,
I'm really confused about the result that I got for the recognition.
When I was using HOG, I got different accuracy for recognition (Based on he parameter im using). I can understand this things.
May be, it's because I have dfferent viewpoint of the training Images and HOG doesnt has a capability for this. So the maximum accuracy that I got is only 40%.
Then Im using SIFT. I got more better result. It's around 70%.
And for Dense SIFT, I got the maximum only 38%.
I dont know, why it's happend. Because Dense SIFT should be more better than SIFT.
So, for this Im trying to use PCA to get the principal features from each descriptor. And then I combine these principal features to do recognition. But I got result more worst. Is it only 30%.
PCA HOG(150,4),PCA SIFT(150,3)=PCA COMBINATION(50,7)
Why this happend? Why DENSE SIFT gives worst result? and Why when I combine all those principal component (from HOG, SIFT and DENSE SIFT), I got more worst result??
And for now, I just doing everything in a sample images. I have 4000 images but for now, I only using 150 images..
I used this small size of sample to try different parameter and after I got this best parameter I'll use it in a whole train images. Is it going to give a same result (compare with the small size images for the sample)??