Main Classification Approaches

Main Classification Approaches

• Generative :

x: input vector (pattern)
y: class label (class)
– Model the joint probability, p(x, y)
– Make predictions by using Bayes rules to calculate p(ylx)
– Pick the most likely label y

• Discriminative :

– Estimate p(ylx) directly (e.g., learn a direct map from inputs x to the class labels y)
– Pick the most likely label y

Pre-processing Step:

Example
(1) Image enhancement
(2) Separate touching or occluding fish
(3) Find the boundary of each fish

Feature Extraction
• Assume a fisherman told us that a sea bass is generally longer than a salmon.
• We can use length as a feature and decide between sea bass and salmon according to a threshold on length.
• How should we choose the threshold?

Multiple Features
• To improve recognition accuracy, we might have to use more than one features at a time.
– Single features might not yield the best performance.
– Using combinations of features might yield better performance.