r ∈ R

Dataset of image patches

t ∈ {0, ⋯, T}

Iteration of ActiveLearn

S^{tr}, S^{te}

Unlabeled training, testing pools

Φ

Training methodology

{S}_{t}^{\mathsf{\text{E}}},{\widehat{S}}_{t}^{\mathsf{\text{E}}}

Eligible samples, annotated samples

{S}_{t,\Phi}^{\mathsf{\text{tr}}}

Samples labeled via Φ at t

{\mathcal{T}}_{t}

Fuzzy classifier using {S}_{t,\Phi}^{\mathsf{\text{tr}}}

k_{1,t}, k_{2,t}

Number of samples in {S}_{t}^{E} from ω_{1}, ω_{2}

M

Number of votes used to generate {\mathcal{T}}_{t}

ω_{1}, ω_{2}

Possible classes of r

τ

Confidence margin

r ↪ ω
_{1}

Membership of r in class ω_{1}

θ

Classifierdependent threshold for {\mathcal{T}}_{t}

\hat{{k}_{1}},\hat{{k}_{2}}

Number of samples in {\widehat{S}}_{t}^{\mathsf{\text{E}}} from ω_{1}, ω_{2}

p_{
t
}(r ↪ ω_{1})

Probability of observing r ↪ ω_{1}

N
_{
t
}

Samples added to training set at t

P
_{Δ}

Model confidence

{\widehat{P}}_{t}

Probability of observing \hat{{k}_{1}} samples

{\mathcal{A}}_{t}

Accuracy of trained classifier at t

\mathcal{L}

Total training cost after T iterations
