Classification of archaeological ceramic fragments using texture and color descriptors

Smith, Patrick J.; Bespalov, Dmitriy; Shokoufandeh, Ali; Jeppson, Patrice L.

doi:10.1109/cvprw.2010.5543523

Cited by 33 publications

(20 citation statements)

References 6 publications

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“…The ceramic sherd database [2010] a used by Smith et al [2010], is ideal for evaluation reasons, because it has significant differences from the pottery sherd database. In this dataset, most of the sherds are highly textured, with smooth surface and high contrast.…”

Section: Resultsmentioning

confidence: 99%

“…Table VIII presents the confusion matrix of our method in the ceramic sherd database, which has a success rate of 78.26%. In the same database, the technique of Smith et al [2010] achieves a 75% success rate using the SIFT texture descriptor and 74% using the TVG descriptor.…”

Section: Evaluation Of the Proposed Technique On The Pottery Sherd Damentioning

confidence: 94%

“…This technique required color calibration with known illuminants and therefore, color estimation was sensitive to lightning variations. Smith et al [2010] proposed a method for ceramic sherd classification based on color and texture characteristics. This is the most recent and accurate sherd classification technique and it was applied to a database that consists of ceramic sherds, most of which are highly textured [Ceramic sherd database 2010].…”

Section: Background and Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Automatic classification of archaeological pottery sherds

Makridis

Daras

2012

J. Comput. Cult. Herit.

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This article presents a novel technique for automatic archaeological sherd classification. Sherds that are found in the field usually have little to no visible textual information such as symbols, graphs, or marks on them. This makes manual classification an extremely difficult and time-consuming task for conservators and archaeologists. For a bunch of sherds found in the field, an expert identifies different classes and indicates at least one representative sherd for each class (training sample). The proposed technique uses the representative sherds in order to correctly classify the remaining sherds. For each sherd, local features based on color and texture information are extracted and are then transformed into a global vector that describes the whole sherd image, using a new bag of words technique. Finally, a feature selection algorithm is applied that locates features with high discriminative power. Extensive experiments were performed in order to verify the effectiveness of the proposed technique and show very promising results.

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Section: Resultsmentioning

confidence: 99%

Section: Evaluation Of the Proposed Technique On The Pottery Sherd Damentioning

confidence: 94%

Section: Background and Related Workmentioning

confidence: 99%

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Automatic classification of archaeological pottery sherds

Makridis

Daras

2012

J. Comput. Cult. Herit.

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“…Smith et al [32] developed a scheme to classify thin-shell ceramics based on colour and texture descriptors in order to aid in vessel reconstructions, using a new feature based on total variation geometry along with SIFT (scale-invariant feature transform). Abadi et al [2] present a system for automatic texture characterization and classification of ceramic pastes, fabrics, and surfaces.…”

Section: Related Workmentioning

confidence: 99%

Image-based search and retrieval for biface artefacts using features capturing archaeologically significant characteristics

Eramian

Walia

Power

et al. 2016

Machine Vision and Applications

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Archaeologists are currently producing huge numbers of digitized photographs to record and preserve artefact finds. These images are used to identify and categorize artefacts and reason about connections between artefacts and perform outreach to the public. However, finding specific types of images within collections remains a major challenge. Often, the metadata associated with images is sparse or is inconsistent. This makes keyword-based exploratory search difficult, leaving researchers to rely on serendipity and slowing down the research process. We present an image-based retrieval system that addresses this problem for biface artefacts. In order to identify artefact characteristics that need to be captured by image features, we conducted a contextual inquiry study with experts in bifaces. We then devised several descriptors for matching images of bifaces with similar artefacts. We evaluated the performance of these descriptors using measures that specifically look at the differences between the sets of images returned by the search system using different descriptors.

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“…In Kampel and Sablatnig (2000), the sherd colour is used, while Smith et al (2010) employ both colour and texture characteristics. Additionally, texture-based features are used and a profile morphological analysis is performed by Li-Ying Qi and Wang Ke-Gang (2010) and Karasik and Smilansky (2011), respectively.…”

Section: Visual Features Extraction Approachmentioning

confidence: 99%