A Novel Sketch Recognition Model based on Convolutional Neural Networks

Kabakuş, Abdullah Talha

doi:10.1109/hora49412.2020.9152911

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…In future work, it would also be interesting to analyze the similarities between human drawings (and their similarities to the exemplars) using artificial neural networks trained on datasets of line drawings, such as the ‘Quick Draw!’ dataset ( Jongejan et al, 2016 ; Ha and Eck, 2017 ; Xu et al, 2021 ; Kabakus, 2020 ). In particular, each of the human drawings (or exemplars) could be fed into such a network, and a feature vector describing the shape derived, allowing an automated quantitative similarity analysis to be performed.…”

Section: Resultsmentioning

confidence: 99%

One-shot generalization in humans revealed through a drawing task

Tiedemann

Morgenstern

Schmidt

et al. 2022

eLife

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Humans have the amazing ability to learn new visual concepts from just a single exemplar. How we achieve this remains mysterious. State-of-the-art theories suggest observers rely on internal ‘generative models’, which not only describe observed objects, but can also synthesize novel variations. However, compelling evidence for generative models in human one-shot learning remains sparse. In most studies, participants merely compare candidate objects created by the experimenters, rather than generating their own ideas. Here, we overcame this key limitation by presenting participants with 2D ‘Exemplar’ shapes and asking them to draw their own ‘Variations’ belonging to the same class. The drawings reveal that participants inferred—and synthesized—genuine novel categories that were far more varied than mere copies. Yet, there was striking agreement between participants about which shape features were most distinctive, and these tended to be preserved in the drawn Variations. Indeed, swapping distinctive parts caused objects to swap apparent category. Our findings suggest that internal generative models are key to how humans generalize from single exemplars. When observers see a novel object for the first time, they identify its most distinctive features and infer a generative model of its shape, allowing them to mentally synthesize plausible variants.

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Section: Resultsmentioning

confidence: 99%

One-shot generalization in humans revealed through a drawing task

Tiedemann

Morgenstern

Schmidt

et al. 2022

eLife

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“…The model achieved a validation accuracy of 72.94%. A sketch recognition model based on (CNN) is presented in Kabakus (2020) . The model was responsible for learning the stroke patterns of sketches and predicting the classes of given sketches.…”

Section: Related Workmentioning

confidence: 99%

Context awareness based Sketch-DeepNet architecture for hand-drawn sketches classification and recognition in AIoT

Ali

Aslam

Kim

et al. 2023

PeerJ Computer Science

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A sketch is a black-and-white, 2-D graphical representation of an object and contains fewer visual details as compared to a colored image. Despite fewer details, humans can recognize a sketch and its context very efficiently and consistently across languages, cultures, and age groups, but it is a difficult task for computers to recognize such low-detail sketches and get context out of them. With the tremendous increase in popularity of IoT devices such as smartphones and smart cameras, etc., it has become more critical to recognize free hand-drawn sketches in computer vision and human-computer interaction in order to build a successful artificial intelligence of things (AIoT) system that can first recognize the sketches and then understand the context of multiple drawings. Earlier models which addressed this problem are scale-invariant feature transform (SIFT) and bag-of-words (BoW). Both SIFT and BoW used hand-crafted features and scale-invariant algorithms to address this issue. But these models are complex and time-consuming due to the manual process of features setup. The deep neural networks (DNNs) performed well with object recognition on many large-scale datasets such as ImageNet and CIFAR-10. However, the DDN approach cannot be carried out for hand-drawn sketches problems. The reason is that the data source is images, and all sketches in the images are, for example, ‘birds’ instead of their specific category (e.g., ‘sparrow’). Some deep learning approaches for sketch recognition problems exist in the literature, but the results are not promising because there is still room for improvement. This article proposed a convolutional neural network (CNN) architecture called Sketch-DeepNet for the sketch recognition task. The proposed Sketch-DeepNet architecture used the TU-Berlin dataset for classification. The experimental results show that the proposed method beats the performance of the state-of-the-art sketch classification methods. The proposed model achieved 95.05% accuracy as compared to existing models DeformNet (62.6%), Sketch-DNN (72.2%), Sketch-a-Net (77.95%), SketchNet (80.42%), Thinning-DNN (74.3%), CNN-PCA-SVM (72.5%), Hybrid-CNN (84.42%), and human recognition accuracy of 73% on the TU-Berlin dataset.

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