Feature extraction metric calculation for image classification
1 view (last 30 days)
Show older comments
I am trying to classify histological images using the bag of features method. In the exampleBagOfFeaturesExtractor the variance is used or the detection metric if a feature detector was used for point selection. I am using different feature extraction techniques and my question is how can I choose which metric(s) to use after the feature extraction.
Another similar question. when I extracted both BRISK and SURF features, I used the normalized variance as a feature metric because the variances from the BRISK features were a lot bigger than the SURFs so basically only the BRISKs were taken into account later at the classification. Was that correct?
2 Comments
Answers (1)
Dima Lisin
on 25 Jan 2016
Edited: Dima Lisin
on 27 Jan 2016
This is strange... You should not have been able to use BRISK descriptors with bagOfFeatures at all. BRISK descriptors are binary bit strings, which cannot not be clustered using K-means.
3 Comments
Dima Lisin
on 26 Jan 2016
Unfortunately, that doesn't work. If you take binaryFeatures objects, convert them to double, and then cluster them, the clusters will not be meaningful. This is because the Euclidean distance between two such vectors has nothing to do with the similarity between the image patches that they describe.
See Also
Categories
Find more on Recognition, Object Detection, and Semantic Segmentation in Help Center and File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!