matlab - How to perform labelling in the 3D extracted features in classification? -


i extracted features many images in 3d. form of extracted features (for example):

 image 1 --> 10 x 12 x 30  image 2 --> 15 x 10 x 30  image 3 --> 13 x 12 x 30  image 4 --> 14 x 19 x 30  .  .  on

in which, first , second dimension changing, third dimension still same (the dimension of feature).

to classification, need label each extracted features each image label. , make easy, have keep features (from each image) in row.

so reshape features 

  feat_reshape= reshape(feature,[size(feature,1)*size(feature,2) size(feature,3)]);

in case, i'll have 2d features matrix.
still, need in 1d.

i tried using "imhist" , keep each feature of each image in each row in matrix. still not give result. may because there many 0 value.

then tried concatenate each column of each features (of each image), become 1 row. problem different size of column in concatenate matrix (element in each row).

for example :

  image 1 --> 10 x 12 x 30 = 3600 elements   image 2 --> 15 x 10 x 30 = 4500 elements

any suggestion,, how can store 3d extracted features , label used in libsvm matlab??

you need method aggregate or pool dense features per image, if label assigned each image. simple histogram 1 approach. alternatives are

  • just resize original 3d image fit fixed size, , compute features.
  • bag-of-words: first cluster per-pixel features , represent every pixel corresponding cluster index (visual words). summarize image histogram.
  • spatial-pyramid: instead of histogram on entire image, split image grids different resolution (1x1, 2x2, etc), summarize features in each cell histogram (or other pooling), , concatenate of them. pooling simple statistic, min, max, mean, etc.

if none works well, suspect feature design. try find best training parameter of svm cross validation (check libsvm documentation).


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