基于草图纹理和形状特征融合的草图识别

张兴园; 黄雅平; 邹琪; 裴艳婷

doi:10.16383/j.aas.c200070

基于草图纹理和形状特征融合的草图识别

doi: 10.16383/j.aas.c200070

1.
北京交通大学计算机与信息技术学院北京 100044

基金项目: 中央高校基本科研业务费专项资金(2020YJS046), 北京市自然科学基金(M22022, L211015)和中国博士后科学基金(2021M690339)资助

详细信息

作者简介:
张兴园：北京交通大学计算机与信息技术学院博士研究生. 主要研究方向为深度学习, 数字图像处理和机器学习. E-mail: 15112071@bjtu.edu.cn

黄雅平：北京交通大学计算机与信息技术学院教授. 主要研究方向为机器学习与认知计算, 人工智能及应用和数字图像处理. 本文通信作者.E-mail: yphuang@bjtu.edu.cn

邹琪：北京交通大学计算机与信息技术学院教授. 主要研究方向为计算机视觉, 人工智能及应用和数字图像处理. E-mail: qzou@bjtu.edu.cn

裴艳婷：北京交通大学计算机与信息技术学院讲师. 主要研究方向为计算机视觉, 人工智能及应用和数字图像处理. E-mail: ytpei@bjtu.edu.cn

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出版历程
- 收稿日期: 2020-02-18
- 录用日期: 2020-05-03
- 网络出版日期: 2022-07-07
- 刊出日期: 2022-09-16

Texture and Shape Feature Fusion Based Sketch Recognition

1.
School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044

Funds: Supported by Fundamental Research Funds for the Central Universities (2020JYS046), Natural Science Foundation of Bei-jing (M22022, L211015) and Postdoctoral Science Foundation of China (2021M690339)

More Information

Author Bio:
ZHANG Xing-Yuan　Ph.D. candidate at the School of Computer and Information Technology, Beijing Jiaotong University. His research interest covers deep learning, digital image processing and machine learning

HUANG Ya-Ping　Professor at the School of Computer and Information Technology, Beijing Jiaotong University. Her research interest covers machine learning and cognitive computing, artificial intelligence and application, and digital image processing. Corresponding author of this paper

ZOU Qi　Professor at the School of Computer and Information Technology, Beijing Jiaotong University. Her research interest covers computer vision, artificial intelligence and application, and digital image processing

PEI Yan-Ting　Lecturer at the School of Computer and Information Technology, Beijing Jiaotong University. Her research interest covers computer vision, artificial intelligence and application, and digital image processing

摘要

摘要: 人类具有很强的草图识别能力. 然而, 由于草图具有稀疏性和缺少细节的特点, 目前的深度学习模型在草图分类任务上仍然面临挑战. 目前的工作只是将草图看作灰度图像而忽略了不同草图类别间的形状表示差异. 提出一种端到端的手绘草图识别模型, 简称双模型融合网络, 它可以通过相互学习策略获取草图的纹理和形状信息. 具体地, 该模型由2个分支组成: 一个分支能够从图像表示(即原始草图)中自动提取纹理特征, 另一个分支能够从图形表示(即基于点的草图)中自动提取形状特征. 此外, 提出视觉注意一致性损失来度量2个分支之间视觉显著图的一致性, 这样可以保证2个分支关注相同的判别性区域. 最终将分类损失、类别一致性损失和视觉注意一致性损失结合完成双模型融合网络的优化. 在两个具有挑战性的数据集TU-Berlin数据集和Sketchy数据集上进行草图分类实验, 评估结果说明了双模型融合网络显著优于基准方法并达到最佳性能.
- 草图分类 /
- 注意力机制 /
- 互学习策略 /
- 图像识别
Abstract: Human has a strong ability to recognize hand-drawn sketches. However, state-of-the-art models on sketch classification tasks remain challenging due to the sparse lines and limited details of sketches. Previous deep neural networks treat sketches as general images and ignore the shape representations for different categories. In this paper, we aim to address the problem by an end-to-end hand-drawn sketch recognition model, named dual-model fusion network, which can capture both texture and shape information of sketches via a mutual learning strategy. Specifically, our model is composed of two branches: one branch can automatically extract texture features from an image-based representation, i.e., the raw sketches, and the other branch can obtain shape information from a graph-based representation, i.e., point-based sketches. Moreover, we propose an attention consistency loss to measure the attention heat-map consistency between the two branches, which can simultaneously enable the same concentration of discriminative regions in the two representations. Finally, the proposed dual-model fusion network is optimized by combining classification loss, category consistency loss and attention consistency loss. We conduct extensive experiments on two challenging data sets, TU-Berlin and Sketchy, for sketch classification tasks. Our dual-model fusion network significantly outperforms baselines, and achieves the new state-of-the-art performance.
- Sketch classification /
- attention mechanism /
- mutual learning strategy /
- image recognition

HTML全文

图 1 本文算法总体框架图

Fig. 1 The overall framework of our method

下载: 全尺寸图片幻灯片

图 2 本文形状特征提取网络的原理框架示意图

Fig. 2 Schematic diagram of Shape-net framework proposed in this paper

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图 3 TU-Berlin数据集上6个类别的受试者工作特征曲线及曲线下面积值

Fig. 3 ROCs and AUC values of 6 classes in the TU-Berlin dataset

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图 4 TU-Berlin数据集上13个类别的分类准确率

Fig. 4 Classification accuracy of 13 classes in the TU-Berlin dataset

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图 5 两种策略在草图点采样的结果示意图

Fig. 5 The point sampling demonstration of two strategies on the sketch

下载: 全尺寸图片幻灯片

表 1 不同算法下在TU-Berlin数据集上分类准确率的比较 (%)

Table 1 Comparison of sketch classification accuracy with different algorithms on the TU-Berlin dataset (%)

方法	图像数量
方法	8	40	64	72
Eitz (KNN hard)	22	33	36	38
Eitz (KNN soft)	26	39	43	44
Eitz (SVM hard)	32	48	53	53
Eitz (SVM soft)	33	50	55	55
FV size 16	39	56	61	62
FV size 16 (SP)	44	60	65	66
FV size 24	41	60	64	65
FV size 24 (SP)	43	62	67	68
SketchPoint	50	68	71	74
AlexNet	55	70	74	75
NIN	51	70	75	75
VGGNet	54	67	75	76
GoogLeNet	52	69	76	77
Sketch-a-Net	58	73	77	78
SketchNet	58	74	77	80
Cousin Network	59	75	78	80
Hybrid CNN	57	75	80	81
LN	58	76	82	82
SSDA	59	76	82	84
DMF-Net	60	77	85	86

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表 2 在TU-Berlin数据集和Sketchy数据集上实现草图分类的网络结构分析 (%)

Table 2 Architecture design analysis for sketch classification on TU-Berlin and Sketchy (%)

方法	TU-Berlin	Sketchy
BN	82.71	85.75
BN + GC	83.93	86.49
BN + AC	84.12	87.07
BN + CC	84.75	87.36
BN + GC + CC	85.47	87.64
BN + AC + CC	85.51	87.71
BN + GC + AC + CC	86.12	88.01

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表 3 利用双分支神经网络的草图分类准确率 (%)

Table 3 Classification accuracy results using two-branch neural networks (%)

方法	TU-Berlin	Sketchy
纹理网络	81.05	83.18
形状网络	70.87	70.43
基础网络	82.71	85.75

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表 4 不同层的分类准确率结果 (%)

Table 4 Classification accuracy results using given feature levels (%)

方法	TU-Berlin	Sketchy
{4}	85.83	87.23
{3, 4}	86.01	87.87
{2, 3, 4}	86.06	87.93
{1, 2, 3, 4}	86.12	88.01

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表 5 2种采样策略在TU-Berlin数据集的分类准确率 (%)

Table 5 Classification accuracy on TU-Berlin dataset using two sampling strategies (%)

方法	分类准确率
均匀采样	86.12
随机采样	86.09

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表 6 不同采样点数对分类准确率的影响 (%)

Table 6 Effects of the point number for the classification accuracy (%)

数据集点数	TU-Berlin	Sketchy
32	81.87	83.37
64	82.75	84.35
128	83.23	84.83
256	84.34	85.90
512	85.42	87.36
600	85.75	87.5
750	86.00	88.00
1024	86.12	88.01
1200	86.13	88.04
1300	86.08	88.01

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