Automatic texture exemplar extraction with jointed deep and broad learning models
Objective Texture exemplar refers to the input samples or templates for texture synthesis or generation that con-tains the desired texture features and structures.Texture synthesis refers to the generation of new texture images by combin-ing or duplicating one or more texture samples.In the texture synthesis task based on the texture exemplar,the diversity and texture structure of the texture exemplar play a decisive role that determines the effect of the texture synthesis task.In the field of computer vision,texture sample diversity is crucial in texture synthesis tasks,which can bring richer,diverse,and realistic appearance to synthesized textures.Simultaneously,it can provide greater creative inspiration and design ideas to artists and designers.At present,texture exemplars can be extracted from multiple sources,such as public texture datasets,Internet picture clips,or photography.That is,texture exemplars are mostly extracted via manual cutting and automatic algorithm extraction.However,not everyone is an artist,and extracting a good texture sample or cutting out a small texture exemplar from an existing image is difficult for ordinary people.In addition,manually cropping and extract-ing high-quality texture samples from a large number of images consumes considerable energy and time for texture artists,and this method is easily driven by subjectivity and limited in diversity.With the development of deep learning algorithms,the currently used state-of-the-art automatic texture exemplar extraction algorithm is the Trimmed T-CNN model based on a convolutional neural network(CNN).It can effectively extract a variety of texture exemplars from the input image.How-ever,the model uses a selective search algorithm to generate a candidate region,and thus,this process is time-consuming and computationally complex,and the model suffers from slow inference speed.Considering the aforementioned reasons,this study is committed to using the rich image resources on the Internet to automatically,quickly,and accurately cut out ideal and diverse texture exemplars from various images,providing users with more choices,and to better meet the needs of texture synthesis task requirements.Method On the basis of the algorithm idea of object detection,we propose an auto-matic texture exemplar extraction algorithm that combines deep learning and broad learning.This algorithm generates can-didate texture exemplar regions through CNN and then uses broad learning for classification.To obtain the ideal texture exemplar,this study first uses the residual feature pyramid network to extract feature maps from the original image,aiming to effectively identify and generate texture exemplar candidates from the input image and then using the region candidate network to automatically and quickly obtain a large number of multi-scale texture exemplar candidate regions.Subse-quently,we leverage a broad learning system to classify the candidate regions of texture exemplars extracted in the previous step.Finally,to obtain the ideal texture exemplar,we designed a scoring criterion based on classification accuracy,distri-bution characteristics,and size,aiming to use the scoring criterion to score the classification results of the broad learning system to screen out the ideal texture exemplars.Result To verify the effectiveness of the proposed method,we first collect a large number of ideal texture exemplars with distinguishable and representative features as a training dataset and divide them into six classes based on size and regularity for experimental verification.A large number of qualitative and quantita-tive experiments are performed in this study.The experimental results show that the accuracy of the model developed in this study reaches 94.66％.Compared with the state-of-the-art method Trimmed T-CNN,the accuracy of the model in this study increases by 0.22％and speed is improved.In particular,for images with resolutions of 512 × 512 pixels,1 024 × 1 024 pixels,and 2 048 × 2 048 pixels,the speed of the algorithm in this study is increased by 1.393 8 s,1.864 3 s,and 2.368 7 s,respectively.Conclusion In this study,we propose an automatic texture exemplar extraction algorithm based on deep learning and broad learning.This algorithm effectively combines the advantages of CNNs and broad learning classifi-cation systems.The experimental results show that our model outperforms several state-of-the-art texture exemplar extrac-tion methods,making texture exemplar extraction results more accurate and efficient.
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