Batik image retrieval based on enhanced micro-structure descriptor

Minarno, Agus Eko; Munarko, Yuda; Bimantoro, Fitri; Kurniawardhani, Arrie; Suciati, Nanik

doi:10.1109/apcase.2014.6924473

Cited by 22 publications

(15 citation statements)

References 5 publications

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“…The best performance achieved a precision and recall of 70% and 75% respectively. In another study of batik image retrieval, similar values of 74% and 89% were obtained [9]. This study applied edge feature orientation combined with micro structure descriptor for enhancing retrieval performance.…”

Section: Introductionsupporting

confidence: 57%

Texture Fusion for Batik Motif Retrieval System

Nurhaida¹,

Wei²,

Zen³

et al. 2016

IJECE

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<p>This paper systematically investigates the effect of image texture features on batik motif retrieval performance. The retrieval process uses a query motif image to find matching motif images in a database. In this study, feature fusion of various image texture features such as Gabor, Log-Gabor, Grey Level Co-Occurrence Matrices (GLCM), and Local Binary Pattern (LBP) features are attempted in motif image retrieval. With regards to performance evaluation, both individual features and fused feature sets are applied. Experimental results show that optimal feature fusion outperforms individual features in batik motif retrieval. Among the individual features tested, Log-Gabor features provide the best result. The proposed approach is best used in a scenario where a query image containing multiple basic motif objects is applied to a dataset in which retrieved images also contain multiple motif objects. The retrieval rate achieves 84.54% for the rank 3 precision when the feature space is fused with Gabor, GLCM and Log-Gabor features. The investigation also shows that the proposed method does not work well for a retrieval scenario where the query image contains multiple basic motif objects being applied to a dataset in which the retrieved images only contain one basic motif object.</p>

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Section: Introductionsupporting

confidence: 57%

Texture Fusion for Batik Motif Retrieval System

Nurhaida¹,

Wei²,

Zen³

et al. 2016

IJECE

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“…The results obtained by EMSD provided better performance than MSD with 5% precision difference and 1% recall when using Corel 5000. While on the test using Corel 10000, it was found 3% precision difference and 1% recall [15].…”

Section: Introductionmentioning

confidence: 89%

Re-Ranking Image Retrieval on Multi Texton Co-Occurrence Descriptor Using K-Nearest Neighbor

Azhar

Minarno

Munarko

2018

EECSI

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Some features commonly used to conduct image retrieval are color, texture and edge. Multi Texton Co-Occurrence Descriptor (MTCD) is a method which uses all three features to perform image retrieval. This method has a high precision when doing retrieval on a patterned image such as Batik images. However, for images focusing on object detection like corel images, its precision decreases. This study proposes the use of KNN method to improve the precision of MTCD method by re-ranking the retrieval results from MTCD. The results show that the method is able to increase the precision by 0.8% for Batik images and 9% for corel images.

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“…This method can extract features of color, texture, and shape simultaneously using texton, then calculates GLCM values as a global representation of the image (Fig 5). Texton was used to detect the co-occurrence of pixel pairs in quantization of RGB and quantization of edge orientation, whereas GLCM was used to represent globally the image in the forms of energy, entropy, contrast and correlation [27]. Several studies of features extraction on batik image for various purposes of the study are shown in Table 1.…”

Section: The Development Of Batik Feature Extraction Methodsmentioning

confidence: 99%