Pre-process data images before training OneClassSVM and decrease number of features
You should try converting them to Principle Components using PCA. Please refer this Analytics Vidya PCA, this should give give you a good understanding.

PCA converts n large vector into p Principle components. 


  First principal component is a linear combination of original predictor variables which captures the maximum variance in the data set


Second PC is also a linear combination of original predictor variables which captures remaining variance. All the succeeding ones follow the same concept.

This way you can select top Principle components which explains good enough cumulative variance in your data