TY - GEN
T1 - Why does the sketch look so Vivid?
AU - Zheng, Ying
AU - Yao, Hongxun
AU - Zhao, Sicheng
AU - Wang, Yasi
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/8/19
Y1 - 2015/8/19
N2 - As an effective and convenient intermediate means, sketch has been playing an important role in expressing human in- tentions. Aware of its significance, plentiful sketch related studies have been conducted, such as sketch based image retrieval and recognition. However, very few works have re- searched the intrinsic factors that make sketch look vivid. In this paper, we explore the key to a good sketch by a weak supervised approach to discovery discriminative patches for different categories of sketches. For each class, we randomly extract tens of thousands of patches at multiple scales, which are represented by Pyramid Histogram of Oriented Gradi- ent (PHOG). Then all patches are thrown into an iterative detection process to find the most discriminative ones. Ex- perimentally, we analyze the visual results and give a reason- able discussion about good and bad sketches. Furthermore, more experimental results on the TU-Berlin sketch bench- mark dataset demonstrate the effectiveness of the proposed method, as compared to other available approaches.
AB - As an effective and convenient intermediate means, sketch has been playing an important role in expressing human in- tentions. Aware of its significance, plentiful sketch related studies have been conducted, such as sketch based image retrieval and recognition. However, very few works have re- searched the intrinsic factors that make sketch look vivid. In this paper, we explore the key to a good sketch by a weak supervised approach to discovery discriminative patches for different categories of sketches. For each class, we randomly extract tens of thousands of patches at multiple scales, which are represented by Pyramid Histogram of Oriented Gradi- ent (PHOG). Then all patches are thrown into an iterative detection process to find the most discriminative ones. Ex- perimentally, we analyze the visual results and give a reason- able discussion about good and bad sketches. Furthermore, more experimental results on the TU-Berlin sketch bench- mark dataset demonstrate the effectiveness of the proposed method, as compared to other available approaches.
KW - Discriminative Patches
KW - Iterative Detection
KW - Pyramid Histogram of Oriented Gra-dient
UR - https://www.scopus.com/pages/publications/84947585076
U2 - 10.1145/2808492.2808529
DO - 10.1145/2808492.2808529
M3 - 会议稿件
AN - SCOPUS:84947585076
T3 - ACM International Conference Proceeding Series
SP - 75
EP - 78
BT - ICIMCS 2015 - Proceedings of the 7th International Conference on Internet Multimedia Computing and Service
A2 - Jain, Ramesh
A2 - Jiang, Shuqiang
A2 - Smith, John
A2 - Sang, Jitao
A2 - Li, Guohui
A2 - Zhang, Tianzhu
A2 - Wang, Shuhui
PB - Association for Computing Machinery
T2 - 7th International Conference on Internet Multimedia Computing and Service, ICIMCS 2015
Y2 - 19 August 2015 through 21 August 2015
ER -