Texture-Based Image Transformations for Improved Deep Learning Classification

Tomas Majtner*, Buda Bajić, Jürgen Herp

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


In this paper, we examine the effect of texture-based image transformation on classification performance. A novel combination of mathematical morphology operations and contrast-limited adaptive histogram equalization is proposed to enhance image textural features. The suggested operations are applied in HSV colour space, where the intensity component is separated from the colour information. Two publicly available, texture-oriented datasets are used for evaluation in this study. The KTH-TIPS2-b dataset is utilised to illustrate the general effectiveness and applicability of the proposed solution on standardized texture images. The Virus Texture dataset is subsequently used to demonstrate a statistically significant classification improvement in a particular biomedical image recognition task
Original languageEnglish
Title of host publication Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2021 : 25th Iberoamerican Congress, CIARP 2021, Porto, Portugal, May 10–13, 2021, Revised Selected Papers
EditorsJoão Manuel R. S. Tavares, João Paulo Papa, Manuel González Hidalgo
Publication date2021
ISBN (Print)978-3-030-93419-4
ISBN (Electronic)978-3-030-93420-0
Publication statusPublished - 2021
Event25th Iberoamerican Congress: CIARP 2021 - Porto, Portugal
Duration: 10. May 202113. May 2021


Conference25th Iberoamerican Congress
SeriesLecture Notes in Computer Science


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