Weather classification from still images using ensemble method
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Authors
Oluwafemi, Ajayi Gbeminiyi
Issue Date
2021-01
Type
Dissertation
Language
en
Keywords
Computer vision , Image classification , Stacking ensemble , Ensemble diversity , Weather identification , Recognition , Machine learning , Image preprocessing , Feature extraction , Heterogenous concept
Alternative Title
Abstract
In the field of computer vision, multi-class outdoor weather classification is a difficult task to
perform due to diversity and lack of distinct weather characteristic or features. This research
proposed a novel framework for identifying different weather scenes from still images using
heterogeneous ensemble methods. The approach was based on construction of unobstructed
opaque cloud coverage (OCC) multi-class weather images; and the introduction of diversity
concept called Selection Based on Accuracy Intuition and diversity (SAID) for the construction of
stacked ensemble models. The stages involve the extraction of histogram of features from different
weather scenes to determine their contribution to the overall performance of the experiment,
training and validating the performance of the model. The blending and boosting of different
weather features using stacked ensemble algorithms shows an average accuracy of over 90% in
recognizing rainy still images and over 80% for sunny, sunrise and sunset still images. Similarly,
the meta-learner of the stacked ensemble model performed better than the individual base learners
of the model. The research presents academic and practitioners a new insight into diversity of
heterogeneous stacked ensemble methods for solving the challenges of weather recognition from
still images.