VFSGroupDataset<FImage> dataset = new VFSGroupDataset<FImage>(
"zip:file:/Users/nhnguyen/Data/newArchive.zip",
ImageUtilities.FIMAGE_READER);
int nTraining = 50;
int nTesting = 5;
GroupedRandomSplitter<String, FImage> splits =
new GroupedRandomSplitter<String, FImage>(dataset, nTraining, 0, nTesting);
GroupedDataset<String, ListDataset<FImage>, FImage> training = splits.getTrainingDataset();
GroupedDataset<String, ListDataset<FImage>, FImage> testing = splits.getTestDataset();
List<FImage> basisImages = DatasetAdaptors.asList(training);
int nEigenvectors = 100;
EigenImages eigen = new EigenImages(nEigenvectors);
eigen.train(basisImages);
我有上面的代码用我自己的数据集测试 EigenImages 教程。我坚持的是,如果在我的数据集中,图像的尺寸不同,比如 92x112 和 100x100 等等,它会抛出 Matrix 异常......当我将批量调整为相同大小时,它会起作用,但是,这些会稍微扭曲图像,我担心会影响准确性。是否可以训练特征识别以接受各种维度的输入?