1 research outputs found
A Color Quantization Optimization Approach for Image Representation Learning
Over the last two decades, hand-crafted feature extractors have been used in
order to compose image representations. Recently, data-driven feature learning
have been explored as a way of producing more representative visual features.
In this work, we proposed two approaches to learn image visual representations
which aims at providing more effective and compact image representations. Our
strategy employs Genetic Algorithms to improve hand-crafted feature extraction
algorithms by optimizing colour quantization for the image domain. Our
hypothesis is that changes in the quantization affect the description quality
of the features enabling representation improvements. We conducted a series of
experiments in order to evaluate the robustness of the proposed approaches in
the task of content-based image retrieval in eight well-known datasets from
different visual properties. Experimental results indicated that the approach
focused on representation effectiveness outperformed the baselines in all the
tested scenarios. The other approach, more focused on compactness, was able to
produce competitive results by keeping or even reducing the final feature
dimensionality until 25% smaller with statistically equivalent performance