# Multiscale Analysis for Improving Texture Classification
## 6 Conclusions  
This research provided a critical study regarding image analysis and manipulation over a spectrum of spatial scales and the complementarity of feature descriptors for texture classification. We stated that employing each descriptor separately may overlook relevant textural information, reducing the classification performance. Moreover, we exploited the pyramid's multiresolution representation as a useful data structure for analyzing and capturing intrinsic details from texture over a spectrum of spatial scales. To produce a feature vector from an image, we combined several descriptors that were proven to be discriminating for the classification, namely, the BiT, information-theoretic measures, GLCM, and Haralick descriptors, to extract gray-level and color features at different resolutions. Such a combination aimed to bring features that characterized the texture to the maximum extent and with some advantages such as rotation-, permutation-, scale-invariance, reduced noise sensitivity, and a generic and high generalization ability, as it provided an effective performance for real-world datasets. The proposed approach outperformed a few state-of-the-art shallow and deep methods. Moreover, the descriptors employed herein were proven complementary as their combination resulted in a better performance than using each separately. However, some features may be redundant after the concatenation into a single feature vector, given the different resolutions, channels, and descriptors. That may cause a downfall in the classification performance. Furthermore, such a concatenation may also lead to the Hughes phenomenon, which explains why we did not include other descriptors such as LBP, HOG, etc. However, we will consider including other descriptors in our future studies. Finally, to circumvent the possible feature redundancy and cope with the increase in dimensionality, we will investigate the impact of incorporating a decision-making multiobjective feature selection. **Author Contributions:** Conceptualization, S.T.M.A., D.S., J.d.M., and A.L.K. ; Methodology, S.T.M.A. ; Writing--original draft preparation, S.T.M.A. and A.L.K. ; Writing--review and editing, S.T.M.A., A.d.S.B.J., and A.L.K. ; Supervision, A.L.K. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was partially funded by the Regroupement Strategique REPARTI-Fonds de Recherche du Quebec--Nature et Technologie (FRQNT) and by the Natural Sciences and Engineering Research Council of Canada (NSERC), Discovery grant number RGPIN-2016-04855. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The KTH-TIPS dataset is available at [[https://www.csc.kth.se/cva](https://www.csc.kth.se/cva)]([https://www.csc.kth.se/cva](https://www.csc.kth.se/cva)) p/databases/kth-tips/index.html, accessed on 12 January 2023. The Outex dataset is available at [[https://color.univ-lille.fr/datasets/extended-outex](https://color.univ-lille.fr/datasets/extended-outex)]([https://color.univ-lille.fr/datasets/extended-outex](https://color.univ-lille.fr/datasets/extended-outex)), accessed on 12 January 2023. The ALOT dataset is available at [[https://color.univ-lille.fr/datasets/alot](https://color.univ-lille.fr/datasets/alot)]([https://color.univ-lille.fr/datasets/alot](https://color.univ-lille.fr/datasets/alot)), accessed on 12 January 2023. The BreakHis dataset is available at [[https://web.inf.ufpr.br/vri/databases/breast-cancer-histopatho](https://web.inf.ufpr.br/vri/databases/breast-cancer-histopatho)]([https://web.inf.ufpr.br/vri/databases/breast-cancer-histopatho](https://web.inf.ufpr.br/vri/databases/breast-cancer-histopatho)) logical-database-breakhis/, accessed on 12 January 2023. The CRC dataset is available at [[https://zenodo.org/record/53169#Y3](https://zenodo.org/record/53169#Y3) MHVy_71 qs]([https://zenodo.org/record/53169#Y3](https://zenodo.org/record/53169#Y3) MHVy_71 qs), accessed on 12 January 2023.