Research Article Refined Color Texture Classification Using CNN and Local Binary Pattern Khalid M. Hosny , 1 Taher Magdy, 2 Nabil A. Lashin , 1 Kyriakos Apostolidis, 3 and George A. Papakostas 3 1 Information Technology Department, Zagazig University, Zagazig 44519, Egypt 2 Computer Science Department, Sinai University, North Sinai, Arish, Egypt 3 MLV Research Group, Department of Computer Science, International Hellenic University, 65404 Kavala, Greece CorrespondenceshouldbeaddressedtoKhalidM.Hosny;k_hosny@yahoo.comandGeorgeA.Papakostas;gpapak@teikav.edu.gr Received 27 May 2021; Revised 20 October 2021; Accepted 23 October 2021; Published 16 November 2021 Academic Editor: Muhammad Haroon Yousaf Copyright © 2021 Khalid M. Hosny et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Representation and classification of color texture generate considerable interest within the field of computer vision. Texture classificationisadifficulttaskthatassignsunlabeledimagesortexturestothecorrectlabeledclass.Somekeyfactorssuchasscaling and viewpoint variations and illumination changes make this task challenging. In this paper, we present a new feature extraction technique for color texture classification and recognition. e presented approach aggregates the features extracted from local binary patterns (LBP) and convolution neural network (CNN) to provide discriminatory information, leading to better texture classificationresults.AlmostalloftheCNNmodelcasesclassifyimagesbasedonglobalfeaturesthatdescribetheimageasawhole to generalize the entire object. LBP classifies images based on local features that describe the image’s key points (image patches). Our analysis shows that using LBP improves the classification task when compared to using CNN only. We test the proposed approachexperimentallyoverthreechallengingcolorimagedatasets(ALOT,CBT,andOutex).eresultsdemonstratedthatour approachimprovedupto25%intheclassificationaccuracyoverthetraditionalCNNmodels.Weidentifyoptimalcombinations foreachdatasetandobtainhighclassificationrates.eproposedapproachisrobust,stable,anddiscriminatoryamongthethree datasets and has shown enhancement in classification and recognition compared to the state-of-the-art method. 1. Introduction e motivation of the usage of CNN models as features descriptors is the ability of the deep neural network to capturethehigh-levelfeaturesthatcanbeakeypointforthe classification of the texture images. Texture plays a signifi- cant role in distinguishing objects in color images. Texture classification involves a two-phase process. e first phase is extracting the features, which provides a feature-based de- scription for each texture type; this phase tends to select featuresunaffectedbyimagetransformation,suchasscaling, translation, and rotation. e second phase tends to rec- ognize the texture from the extracted features. Texture recognition is increasingly set as a critical issue in computer vision that has many applications such as biomedical image processing[1–5],objectdetection[6–8],andremotesensing [9–11] application fields. e scale-invariant feature transform (SIFT), proposed by Lowe [12], is a common sparse descriptor. Mikolajczyk and Schmid changed the SIFT descriptor by altering the gradient location orientation grid and the quantization parameters of the histograms [13]. Chen et al. [14] proposed a robust dis- criminative descriptor called Weber local descriptor (WLD) based on human perception. Gabor wavelet descriptor is considered one of the most widely used dense descriptors [15]. e Gabor wavelet has extensively used image recognition applications such as face recognition, scene analysis, motion tracking, and face recognition [16]. Ershad and Fekri [17] proposed a new approach to analyze the texture based on its grain components and classify it from grain components Hindawi Mathematical Problems in Engineering Volume 2021, Article ID 5567489, 15 pages https://doi.org/10.1155/2021/5567489