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A patch-based sparse representation for sketch recognition

Qi, Y., Zhang, H., Song, Yi-Zhe and Tan, Z. (2015) A patch-based sparse representation for sketch recognition In: 2014 4th IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC 2014), 19-21 Sep 2014, Beijing, China.

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Categorizing free-hand human sketches has profound implications in applications such as human computer interaction and image retrieval. The task is non-trivial due to the iconic nature of sketches, signified by large variances in both appearance and structure when compared with photographs. One of the most fundamental problems is how to effectively describe a sketch image. Many existing descriptors, such as histogram of oriented gradients (HOG) and shape context (SC), have achieved great success. Moreover, some works have attempted to design features specifically engineered for sketches, such as symmetric-aware flip invariant sketch histogram (SYM-FISH). We present a novel patch-based sparse representation (PSR) for describing sketch image and it is evaluated under a sketch recognition framework. Extensive experiments on a large scale human drawn sketch dataset demonstrate the effectiveness of the proposed method.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Qi, Y.
Zhang, H.
Tan, Z.
Date : February 2015
DOI : 10.1109/ICNIDC.2014.7000322
Uncontrolled Keywords : Patch-based sparse representation; Sketch recognition
Related URLs :
Additional Information : Printed proceedings published by Curran Associates Inc.
Depositing User : Clive Harris
Date Deposited : 12 Aug 2019 13:39
Last Modified : 12 Aug 2019 13:41

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