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Proceedings Paper

Multi-object sketch segmentation using convolutional object detectors
Author(s): Momina Moetesum; Osama Zeeshan; Imran Siddiqi
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Paper Abstract

Segmentation of constituent shapes is a vital yet challenging step in any automated sketch analysis and interpretation system. Conventional stroke level sketch segmentation techniques perform well on a single shape, nevertheless, their performance degrades in a cluttered multi-object sample. On the contrary, object level techniques rely on proximity based perceptual grouping for shapes with disjoint constituent parts, which requires high level semantic knowledge. To overcome these challenges, we propose the use of state-of-the-art convolutional object detectors for the detection and segmentation of hand drawn shapes from offline samples of a neuropsychological drawing test i.e. Bender Gestalt Test (BGT). Experiments with different combinations of convolutional meta-architectures and feature extractors show that such networks can successfully be employed for sketch segmentation purposes even with limited training data and resources. Amongst all network combinations under evaluation in this study, Faster R-CNNs with ResNet-101 as feature extractor, outperform others by achieving precision, recall and F-measure values of 92.93%, 95.24% and 94.07% respectively.

Paper Details

Date Published: 6 May 2019
PDF: 6 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 1106929 (6 May 2019); doi: 10.1117/12.2524293
Show Author Affiliations
Momina Moetesum, Bahria Univ. (Pakistan)
Osama Zeeshan, Bahria Univ. (Pakistan)
Imran Siddiqi, Bahria Univ. (Pakistan)


Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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