Reinforced AdaBoost Learning for Object Detection with Local Pattern Representations

Algorithm 1

Proposed update procedure of a classifier based on a reinforced Adaboost learning.

: Number of positive samples, : Number of negative samples

: LPR of ith positive sample, : LPR of ith negative sample

: labels (+1: positive, −1: negative)

(1) Initialize sample weights

(2) For do

(a) Generate histograms of LPR from positive samples and negative samples

: indicator function that takes 1 if the argument is true and 0 otherwise

(b) Compute error rate

(c) Select the best pixel location with the smallest error rate

: the maximum number of pixel locations allowed

: the set of pixel locations already chosen before,

(d) Create a lookup table for the weak classifier of pixel location

,

(e) Update the sample weights & Normalization

,

(3) Obtain pixel classifier of a single pixel location x

Constructed strong classifier

We are committed to sharing findings related to COVID-19 as quickly as possible. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. Review articles are excluded from this waiver policy. Sign up here as a reviewer to help fast-track new submissions.