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Sketch-based image retrieval via Siamese convolutional neural network

Qi, Y., Song, Yi-Zhe, Zhang, H. and Liu, J. (2017) Sketch-based image retrieval via Siamese convolutional neural network In: 2016 IEEE International Conference on Image Processing (ICIP 2016), 25-28 Sep 2016, Phoenix, Arizona, USA.

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Sketch-based image retrieval (SBIR) is a challenging task due to the ambiguity inherent in sketches when compared with photos. In this paper, we propose a novel convolutional neural network based on Siamese network for SBIR. The main idea is to pull output feature vectors closer for input sketch-image pairs that are labeled as similar, and push them away if irrelevant. This is achieved by jointly tuning two convolutional neural networks which linked by one loss function. Experimental results on Flickr15K demonstrate that the proposed method offers a better performance when compared with several state-of-the-art approaches. © 2016 IEEE.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Qi, Y.
Zhang, H.
Liu, J.
Date : March 2017
DOI : 10.1109/ICIP.2016.7532801
Uncontrolled Keywords : SBIR; Siamese CNN; Image edge detection; Neural networks; Image retrieval; Feature extraction; Training; Shape; Visualization
Related URLs :
Additional Information : Printed proceedings published by Curran Associates Inc.
Depositing User : Clive Harris
Date Deposited : 12 Aug 2019 07:28
Last Modified : 12 Aug 2019 08:38

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