Authors:
Fabian Bürger
and
Josef Pauli
Affiliation:
Universität Duisburg-Essen, Germany
Keyword(s):
Manifold Learning, Model Selection, Evolutionary Optimization, Object Recognition.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
Abstract:
The development of image-based object recognition systems with the desired performance is – still – a challenging
task even for experts. The properties of the object feature representation have a great impact on the
performance of any machine learning algorithm. Manifold learning algorithms like e.g. PCA, Isomap or
Autoencoders have the potential to automatically learn lower dimensional and more useful features. However,
the interplay of features, classifiers and hyperparameters is complex and needs to be carefully tuned
for each learning task which is very time-consuming, if it is done manually. This paper uses a holistic optimization
framework with feature selection, multiple manifold learning algorithms, multiple classifier concepts
and hyperparameter optimization to automatically generate pipelines for image-based object classification. An
evolutionary algorithm is used to efficiently find suitable pipeline configurations for each learning task. Experiments
show the effectiveness
of the proposed representation and classifier tuning on several high-dimensional
object recognition datasets. The proposed system outperforms other state-of-the-art optimization frameworks.
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