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A genetic algorithm approach for image representation learning through color quantization

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Abstract

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%.

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Acknowledgments

This study was financed in part by: the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001; the Brazilian National Council for Scientific and Technological Development (CNPq)—grants #424700/2018-2 and #311395/2018-0; and the Minas Gerais Research Foundation (FAPEMIG)—grant APQ-00449-17. Authors are also grateful to CAPES (grant #88881.145912/2017-01), São Paulo Research Foundation—FAPESP (grants #2014/12236-1, #2015/24494-8, #2016/50250-1, and #2017/20945-0) and the FAPESP—Microsoft Virtual Institute (grants #2013/50155-0, #2013/50169-1, and #2014/50715-9).

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Authors and Affiliations

  1. Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos, 6627, Belo Horizonte, MG, 31270-010, Brazil

    Erico M. Pereira & Jefersson A. dos Santos

  2. Institute of Computing, University of Campinas, Av. Albert Einstein, 1251, Campinas, SP, Brazil

    Ricardo da S. Torres

  3. Department of ICT and Natural Sciences, NTNU – Norwegian University of Science and Technology, Ålesund, Norway

    Ricardo da S. Torres

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  1. Erico M. Pereira
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Correspondence to Jefersson A. dos Santos.

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Pereira, E.M., Torres, R.d.S. & dos Santos, J.A. A genetic algorithm approach for image representation learning through color quantization. Multimed Tools Appl 80, 15315–15350 (2021). https://doi.org/10.1007/s11042-020-10194-z

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