Advantages/Disadvantages
8 important questions on Advantages/Disadvantages
BoF Advantages/Disadvantages
+ Robust matching of visual word instances
+ Fixed length feature vector
+ Good performance (simplicity)
- Loss of spatial position and object configuration (so, not suitable for recognise or localise images)
- Quantisation loses information
Cross-correlation Advantages/Disadvantages
+ Commutative (F * G = G * F)
- Difficult when working with the edge:
+ Ignore
+ Mirroring
+ Wrap around
+ Copy edge (assume image continues like on the edge)
Gradient/Image derivative Advantages/Disadvantages
- Different lightning or camera position can cause 2 images of the same scene to have drastically different pixel values
+ Compute texture or feature signatures based on gradient images computed from the original images.
- Higher grades + faster learning
- Never study anything twice
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Harris Corner Detection Advantages/Disadvantages
+ Invariant to intensity changes (partly to multiplicative intensity change)
- Not invariant to image scaling
K-means Advantages/Disadvantages
+ It is parallelizable
+ Works well for a large range of problems without need of tuning
- Variations in cluster size
- Variations in cluster densities
- Non-globular shapes
- Number of clusters need to be decided beforehand: inappropriate choice will give poor clustering results
K-nn Advantages/Disadvantages
+ Easy to integrate new images
+ Easy to integrate new classes
- But expensive at test time
- K has to be chosen beforehand
- Incorrect classifier when the class distribution is skewed.
+ Weight the classification
Panorama Advantages/Disadvantages
- Reliable & Destinctive descriptor: For each point correctly recognize the corresponding one
SIFT Advantages/Disadvantages
+ Descriptiveness
Robust matching technique (to noise):
+ Scale invariant
+ Rotation invariant
+ Intensity change invariant
- Not invariant to light color change
- Too big when storing each SIFT (128 dimensions), we better can use K-means Clustering to save a lot of space.
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