See the published version of this preprint at EURASIP Journal on Advances in Signal Processing.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
Research
Lei ZHANG
Digital Media College of Chongqing College of Electronic Engineering
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
In the task of hand-drawn sketch recognition, traditional deep learning methods have the insufficient of feature extraction and low recognition rate. To improve the insufficient, a novel algorithm based on double channel convolution neural network is proposed. First of all, the hand-drawn sketch is preprocessed to get a smooth sketch. And the contour extraction algorithm is adopted to get the contour of the sketch. The sketch and its contour are then used as input images of the CNN respectively. Finally, through performing feature fusion at the full connection layer, the classification results are obtained using the softmax classifier. The experimental results show that the proposed method can effectively improve the recognition rate of hand-drawn sketch.
Keywords
Hand-drawn sketch recognition, multi-channel, convolution neural network, deep leaning
Figure 1
Figure 2
Figure 3
Figure 4
Loading...
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at EURASIP Journal on Advances in Signal Processing.
Version 1
Posted 17 May, 2021
No community comments so far
Editorial decision: Major revision
On 31 May, 2021
Reviewer #2 agreed
On 23 May, 2021
Reviews received
Received 16 May, 2021
Reviewer #1 agreed
On 15 May, 2021
Reviewers invited
Invitations sent on 15 May, 2021
Editor assigned
On 13 May, 2021
Submission checks complete
On 13 May, 2021
Editor invited
On 13 May, 2021
First submitted
On 10 May, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Health Economics & Outcomes Research
Learn more about our company.
A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
See the published version of this preprint at EURASIP Journal on Advances in Signal Processing.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Lei ZHANG
Digital Media College of Chongqing College of Electronic Engineering
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at EURASIP Journal on Advances in Signal Processing.
Version 1
Posted 17 May, 2021
No community comments so far
Editorial decision: Major revision
On 31 May, 2021
Reviewer #2 agreed
On 23 May, 2021
Reviews received
Received 16 May, 2021
Reviewer #1 agreed
On 15 May, 2021
Reviewers invited
Invitations sent on 15 May, 2021
Editor assigned
On 13 May, 2021
Submission checks complete
On 13 May, 2021
Editor invited
On 13 May, 2021
First submitted
On 10 May, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Health Economics & Outcomes Research
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at EURASIP Journal on Advances in Signal Processing.
In the task of hand-drawn sketch recognition, traditional deep learning methods have the insufficient of feature extraction and low recognition rate. To improve the insufficient, a novel algorithm based on double channel convolution neural network is proposed. First of all, the hand-drawn sketch is preprocessed to get a smooth sketch. And the contour extraction algorithm is adopted to get the contour of the sketch. The sketch and its contour are then used as input images of the CNN respectively. Finally, through performing feature fusion at the full connection layer, the classification results are obtained using the softmax classifier. The experimental results show that the proposed method can effectively improve the recognition rate of hand-drawn sketch.
Figure 1
Figure 2
Figure 3
Figure 4
Loading...