Computer Vision | Matlab
This project uses different types of feature representation and classification to identify the scene type of a given image. A training set of 1500 labelled scenes are used as a sample set to identify the scene type of test images. The feature representation techniques used are
- Bag of words
- Gist along with Sift descriptors
- Sift descriptors
- Tiny images
And the classification techniques used are
- Support Vector Machine
- Naive-Bayes Nearest Neighbor
- Nearest Neighbor classifier
The maximum accuracy achieved was 0.685.
Results visualization for good performing bag of sift – svm pipeline.
Accuracy (mean of diagonal of confusion matrix) is 0.685
Links: Project Report