Abstract
Sketches have been employed since the ancient era of cave paintings for simple illustrations to represent real-world entities and communication. The abstract nature and varied artistic styling make automatic recognition of these drawings more challenging than other areas of image classification. Moreover, the representation of sketches as a sequence of strokes instead of raster images introduces them at the correct abstract level. However, dealing with images as a sequence of small information makes it challenging. In this paper, we propose a Transformer-based network, dubbed as AttentiveNet, for sketch recognition. This architecture incorporates ordinal information to perform the classification task in real-time through vector images. We employ the proposed model to isolate the discriminating strokes of each doodle using the attention mechanism of Transformers and perform an in-depth qualitative analysis of the isolated strokes for classification of the sketch. Experimental evaluation validates that the proposed network performs favorably against state-of-the-art techniques.
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Notes
Official TensorFlow implementation of RNN for QuickDraw
References
Abualigah L (2020) Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications. Neural Comput Applic pp 1–21
Abualigah L, Diabat A (2020) A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments. Clust Comput pp 1–19
Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773–4795
Abualigah LM, Khader AT, Hanandeh ES (2018) A combination of objective functions and hybrid krill herd algorithm for text document clustering analysis. Eng Appl Artif Intell 73:111–125
Abualigah LM, Khader AT, Hanandeh ES (2018) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48(11):4047–4071. https://doi.org/10.1007/s10489-018-1190-6
Abualigah LM, Khader AT, Hanandeh ES (2018) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456–466
Abualigah LMQ (2018) Feature selection and enhanced krill herd algorithm for text document clustering, 1st edn, Springer Publishing Company, Incorporated
Arandjelović R, Sezgin TM (2011) Sketch recognition by fusion of temporal and image-based features. Pattern Recogn 44(6):1225–1234
Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv:1409.0473
Dehghani M, Gouws S, Vinyals O, Uszkoreit J, Kaiser Ł (2018) Universal transformers. arXiv:1807.03819
Eitz M, Hays J, Alexa M (2012) How do humans sketch objects?. ACM Trans Graph 31(4):44–1
Eitz M, Richter R, Boubekeur T, Hildebrand K, Alexa M (2012) Sketch-based shape retrieval. ACM Trans Graph (TOG) 31(4):31
Graves A (2013) Generating sequences with recurrent neural networks. arXiv:1308.0850
Ha D, Eck D (2017) A neural representation of sketch drawings. arXiv:1704.03477
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Huang Z, Fu H, Lau RW (2014) Data-driven segmentation and labeling of freehand sketches. ACM Trans Graph (TOG) 33(6):175
Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
LaViola JJ Jr, Zeleznik RC (2004) Mathpad 2: a system for the creation and exploration of mathematical sketches. ACM Trans Graph (TOG) 23 (3):432–440
Li K, Pang K, Song J, Song YZ, Xiang T, Hospedales TM, Zhang H (2018) Universal sketch perceptual grouping. In: Proceedings of the european conference on computer vision (ECCV), pp 582–597
Li L, Fu H, Tai CL (2018) Fast sketch segmentation and labeling with deep learning. IEEE Comput Graph Appl 39(2):38–51
Li Y, Hospedales TM, Song YZ, Gong S (2015) Free-hand sketch recognition by multi-kernel feature learning. Comput Vis Image Underst 137:1–11
Liu P, Yu H, Cang S (2019) Adaptive neural network tracking control for underactuated systems with matched and mismatched disturbances. Nonlinear Dynam 98(2):1447–1464
Lu T, Tai CL, Su F, Cai S (2005) A new recognition model for electronic architectural drawings. Comput Aided Des 37(10):1053–1069
Ouyang TY, Davis R (2011) Chemink: a natural real-time recognition system for chemical drawings. In: Proceedings of the 16th international conference on Intelligent user interfaces, ACM , pp 267–276
Sangkloy P, Burnell N, Ham C, Hays J (2016) The sketchy database: learning to retrieve badly drawn bunnies. ACM Trans Graph (TOG) 35 (4):119
Sarvadevabhatla RK, Babu RV (2015) Freehand sketch recognition using deep features. arXiv:1502.00254
Sarvadevabhatla RK, Surya S, Mittal T, Babu RV (2018) Game of sketches: deep recurrent models of pictionary-style word guessing. In: Thirty-second AAAI conference on artificial intelligence
Schneider RG, Tuytelaars T (2014) Sketch classification and classification-driven analysis using fisher vectors. ACM Trans Graph (TOG) 33(6):174
Seddati O, Dupont S, Mahmoudi S (2015) Deepsketch: deep convolutional neural networks for sketch recognition and similarity search. In: 2015 13th international workshop on content-based multimedia indexing (CBMI), IEEE, pp 1–6
Seddati O, Dupont S, Mahmoudi S (2017) Deepsketch 3. Multimed Tools Appl 76(21):22,333–22,359
Sert M, Boyacı E (2019) Sketch recognition using transfer learning. Multimed Tools Appl 78(12):17,095–17,112
Sezgin TM, Davis R (2008) Sketch recognition in interspersed drawings using time-based graphical models. Comput Graph 32(5):500–510
Song J, Pang K, Song YZ, Xiang T, Hospedales TM (2018) Learning to sketch with shortcut cycle consistency. In: Proceedings of the IEEE conference on computer vision and pattern recognition , pp 801–810
Sun L, Zhao C, Yan Z, Liu P, Duckett T, Stolkin R (2018) A novel weakly-supervised approach for rgb-d-based nuclear waste object detection. IEEE Sensors J 19(9):3487–3500
Sun Z, Wang C, Zhang L, Zhang L (2012) Free hand-drawn sketch segmentation. In: European conference on computer vision. Springer, New York, pp 626–639
Sutherland IE (1964) Sketchpad a man-machine graphical communication system. Simulation 2(5) R–3
Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9
Tang Z, Yu H, Lu C, Liu P, Jin X (2019) Single-trial classification of different movements on one arm based on erd/ers and corticomuscular coherence. IEEE Access 7:128,185–128,197
Tang ZC, Li C, Wu JF, Liu PC, Cheng SW (2019) Classification of eeg-based single-trial motor imagery tasks using a b-csp method for bci. Front Inform Technol Electron Eng 20(8):1087–1098
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998–6008
Wang F, Kang L, Li Y (2015) Sketch-based 3d shape retrieval using convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1875–1883
Wang X, Chen X, Zha Z (2018) Sketchpointnet: a compact network for robust sketch recognition. In: 2018 25th IEEE international conference on image processing (ICIP), IEEE, pp 2994–2998
Xu P, Huang Y, Yuan T, Pang K, Song YZ, Xiang T, Hospedales TM, Ma Z, Guo J (2018) Sketchmate: deep hashing for million-scale human sketch retrieval. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8090–8098
Yang Y, Hospedales TM (2015) Deep neural networks for sketch recognition. arXiv:1501.07873 1(2), 3
Yanık E, Sezgin TM (2015) Active learning for sketch recognition. Comput Graph 52:93–105
Yu Q, Yang Y, Liu F, Song YZ, Xiang T, Hospedales TM (2017) Sketch-a-net: a deep neural network that beats humans. Int J Comput Vision 122(3):411–425
Zhang H, Liu S, Zhang C, Ren W, Wang R, Cao X (2016) Sketchnet: sketch classification with web images. In: Proceedings of the IEEE conference on computer vision and pattern recognition , pp 1105–1113
Zhang J, Chen Y, Li L, Fu H, Tai CL (2018) Context-based sketch classification. In: Proceedings of the joint symposium on computational aesthetics and sketch-based interfaces and modeling and non-photorealistic animation and rendering, ACM, p 3
Zhang X, Huang Y, Zou Q, Pei Y, Zhang R, Wang S (2019) A hybrid convolutional neural network for sketch recognition. Pattern Recognition Letters
Zhao P, Liu Y, Lu Y, Xu B (2019) A sketch recognition method based on transfer deep learning with the fusion of multi-granular sketches. Multimed Tools Appl 78(24):35,179–35,193
Zou C, Yu Q, Du R, Mo H, Song YZ, Xiang T, Gao C, Chen B, Zhang H (2018) Sketchyscene: richly-annotated scene sketches. In: Proceedings of the european conference on computer vision (ECCV), pp 421–436
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Parihar, A.S., Jain, G., Chopra, S. et al. SketchFormer: transformer-based approach for sketch recognition using vector images. Multimed Tools Appl 80, 9075–9091 (2021). https://doi.org/10.1007/s11042-020-09837-y
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DOI: https://doi.org/10.1007/s11042-020-09837-y