Visual textures are important for numerous scientific research domains like drug discovery, medical imaging, nano-scale chemical imaging studies, and molecular modeling. Texture studies in identifying drug shelf life and vegetable fungal studies are also upcoming domains of research to maintain the highest quality of products. Over the decades, the surface texture, carbon capturing, and chemical compounds like enamel coating in various industrial applications have been evaluated to improve visual analysis methods. Hence, the proposed study focused on the improvement of visual texture analysis. In image processing tasks, texture is the crucial component of using humane visual models for differentiating different targets in a necessary scenario. This study uses a variational model founded on the standard set for cross-hatched texture segmentation. The proposed model’s functionality is endorsed on the Brodatz texture dataset in this research. The cross-hatched texture segmentation in the reduced image resolution texture is challenging because of the computational and storage requisites. The previously mentioned issues have been treated by applying a variational model according to the standard set that allows successful segmentation in low- and high-resolution graphics with an assortment of the filter size. In the proposed model, the multi-resolution characteristic acquired from the frequency domain filters improves the significant difference among the areas of cross-hatched textures with low-intensity disparities. Then, the resulting graphics are bundled by way of a level set-based active contour model that tackles the segmentation of cross-hatched texture imagery. The noise introduced during the segmentation procedure is eradicated by morphological refinement. The trials carried out on the Brodatz texture dataset exhibited the performance of the proposed model and the outcomes attained are authenticated regarding Intersection over the Union (IoU) index, accuracy, precision, f1-score, and recall. The intensive, unique analysis reveals that the proposed model systematically segments the region of interest in close correspondence with the main image. The proposed segmentation model with a multi-support vector machine has accomplished a classification accuracy of 99.82%, which is remarkable compared to the comparative model, like a modified convolutional neural network with a whale optimization algorithm. The proposed model nearly confirmed 0.11% betterment in classification accuracy appropriate to the existing model. Hence, the proposed study can be used for texture analysis as a generic solution for medical, drug/molecular chemical manufacturing, industrial, and agricultural domains.