Graph-Based Shape Similarity of Petroglyphs

Seidl, Markus; Wieser, Ewald; Zeppelzauer, Matthias; Pinz, Axel; Breiteneder, Christian

doi:10.1007/978-3-319-16178-5_9

Cited by 10 publications

(11 citation statements)

References 31 publications

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“…Subsequently, Deufemia et al [7] use Fourier descriptors to detect and classify petroglyphs from full scenes which stem from manual tracings. Seidl et al [18], [17] combine skeletal descriptors with shape descriptors for petroglyph classification.…”

Section: Related Workmentioning

confidence: 99%

“…Existing work on the automated analysis of rock-art deals mostly with the recognition and classification of pre-segmented petroglyph shapes [23], [24], [8], [6], [7], [18], [17]. The automatic segmentation of petroglyph shapes is an important pre-processing step in this context which is still unsolved and even considered infeasible by Zhu et al [23].…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Interactive segmentation of rock-art in high-resolution 3D reconstructions

Zeppelzauer

Poier

Seidl

et al. 2015

2015 Digital Heritage

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Petroglyphs (rock engravings) are important artifacts for the documentation and analysis of early human life. Recent improvements in 3D scanning and 3D reconstruction enable the accurate 3D reconstruction of petroglyphs from rock surfaces at sub-millimeter resolution. To enable the indexing, matching, and recognition of petroglyphs in petroglyph databases, the shapes must first be segmented from the reconstructed rock surface. The absence of robust 3D segmentation methods for petroglyphs leaves a gap in the digital processing workflow. In this paper, we present a semi-automatic method for petroglyph segmentation for high-resolution 3D surface reconstructions. A comprehensive evaluation shows that our method is able to robustly segment petroglyphs with high accuracy and that the incorporation of 3D information is crucial to solve the segmentation problem. The presented method represents a major step towards the completion of a full 3D digital processing workflow of petroglyphs.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Interactive segmentation of rock-art in high-resolution 3D reconstructions

Zeppelzauer

Poier

Seidl

et al. 2015

2015 Digital Heritage

Self Cite

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“…Petroglyph, in other words rock-art, analysis [8,28,[31][32][33]46] is another related topic to our glyph recognition task. For petroglyph segmentation that can be considered as foreground/background classification of pixels, Seidl et al [31] studied various combinations of traditional textural features.…”

Section: Related Workmentioning

confidence: 99%

How to Tell Ancient Signs Apart? Recognizing and Visualizing Maya Glyphs with CNNs

Can

Odobez

Gática-Pérez

2018

J. Comput. Cult. Herit.

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Thanks to the digital preservation of cultural heritage material, multimedia tools, e.g. based on automatic visual processing, enable to considerably ease the work of scholars in the humanities and help them to perform quantitative analysis of their data. In this context, this paper assesses three different Convolutional Neural Network (CNN) architectures along with three learning approaches to train them for hieroglyph classification, which is a very challenging task due to the limited availability of segmented ancient Maya glyphs. More precisely, the first approach, the baseline, relies on pretrained networks as feature extractor. The second one investigates a transfer learning method by fine-tuning a pretrained network for our glyph classification task. The third approach considers directly training networks from scratch with our glyph data. The merits of three different network architectures are compared: a generic sequential model (i.e. LeNet), a sketch-specific sequential network (i.e. Sketch-a-Net), and the recent Residual Networks. The sketch-specific model trained from scratch outperforms other models and training strategies. Even for a challenging 150-class classification task, this model achieves 70.3% average accuracy and proves itself promising in case of small amount of cultural heritage shape data. Furthermore, we visualize the discriminative parts of glyphs with the recent Grad-CAM method, and demonstrate that the discriminative parts learned by the model agrees in general with the expert annotation of the glyph specificity (diagnostic features). Finally, as a step towards systematic evaluation of these visualizations, we conduct a perceptual crowdsourcing study. Specifically, we analyze the interpretability of the representations from Sketch-a-Net and ResNet-50. Overall, our paper takes two important steps towards providing tools to scholars in the digital humanities: increased performance for automation, and improved interpretability of algorithms. CCS Concepts: • Computing methodologies → Shape representations; Neural networks; Object identification; • Applied computing → Arts and humanities;

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“…Image Analysis [19][20][21][22][23][24] Handwritten Document Analysis [25][26][27][28][29][30][31][32][33] Biometrics [34][35][36][37][38][39][40] Bio-and Chemoinformatics [41][42][43][44][45][46] Knowledge and Process Management [47][48][49][50] Malware Detection [51][52][53][54] Other Applications [55][56][57][58] Fig. 1: Taxonomy of the reviewed application fields and papers that use the framework for Bipartite Graph Edit Distance (BP).…”

Section: Applications Of the Bipartite Graph Edit Distance (Bp)mentioning

confidence: 99%

“…In [21] graphs are used to represent thinned images of archaeological structures (so called Kites) in order to find similar structures in large aerial image databases. Finally, in [22] the BP framework has been employed for shoe print classification, while in [23] the BP algorithm is used for the computation of similarities between petroglyphs.…”

Section: Image Analysismentioning

confidence: 99%

A Survey on Applications of Bipartite Graph Edit Distance

Stauffer¹,

Tschachtli²,

Fischer³

et al. 2017

Graph-Based Representations in Pattern Recognition

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About ten years ago, a novel graph edit distance framework based on bipartite graph matching has been introduced. This particular framework allows the approximation of graph edit distance in cubic time. This, in turn, makes the concept of graph edit distance also applicable to larger graphs. In the last decade the corresponding paper has been cited more than 360 times. Besides various extensions from the methodological point of view, we also observe a great variety of applications that make use of the bipartite graph matching framework. The present paper aims at giving a first survey on these applications stemming from six different categories (which range from document analysis, over biometrics to malware detection).

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Graph-Based Shape Similarity of Petroglyphs

Cited by 10 publications

References 31 publications

Interactive segmentation of rock-art in high-resolution 3D reconstructions

Interactive segmentation of rock-art in high-resolution 3D reconstructions

How to Tell Ancient Signs Apart? Recognizing and Visualizing Maya Glyphs with CNNs

A Survey on Applications of Bipartite Graph Edit Distance

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