  Over the last decades, hand-crafted feature extractors have been used to
encode image visual properties into feature vectors. Recently, data-driven
feature learning approaches have been successfully explored as alternatives for
producing more representative visual features. In this work, we combine both
research venues, focusing on the color quantization problem. We propose two
data-driven approaches to learn image representations through the search for
optimized quantization schemes, which lead to more effective feature extraction
algorithms and compact representations. Our strategy employs Genetic Algorithm,
a soft-computing apparatus successfully utilized in
Information-retrieval-related optimization problems. We hypothesize that
changing the quantization affects the quality of image description approaches,
leading to effective and efficient representations. We evaluate our approaches
in content-based image retrieval tasks, considering eight well-known datasets
with different visual properties. Results indicate that the approach focused on
representation effectiveness outperformed baselines in all tested scenarios.
The other approach, which also considers the size of created representations,
produced competitive results keeping or even reducing the dimensionality of
feature vectors up to 25%.
