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Your PCA object basically creates a new coordinate system for your data, such that the first principal component captures the 'most interesting data', the second principal component the second most, etc. With most interesting data I mean that the data in this direction has the highest variation.
If you have projected your samples for your SVM classifier using some PCA projection, you need to do that exact same projection if you want to classify a new image. Otherwise you'd be comparing different coordinate systems with eachother, like comparing meters with yards, celsius with fahrenheit, apples with pears.
