Image Retrieval Based on Multi Structure Co-occurrence Descriptor

Minarno, Agus Eko; Kurniawardhani, Arrie; Bimantoro, Fitri

doi:10.12928/telkomnika.v14i3.3292

Cited by 9 publications

(8 citation statements)

References 6 publications

(2 reference statements)

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“…Gray level co-occurrence matrix (GLCM) is defined as a matrix whose elements consist of pairs of pixels having a certain brightness level, where pairs of pixels are separated by distance d, with a θ angle [19][20][21]. GLCM is considered as the most common method based on the static approach for texture extraction and GLCM approach usually presented in a symmetrical matrix, increasing the required computational time [22].…”

Section: Gray Level Co-occurence Matrix (Glcm)mentioning

confidence: 99%

Classification of batik patterns using K-Nearest neighbor and support vector machine

et al. 2020

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This study is proposed to compare which are the better method to classify Batik image between K-Nearest neighbor and Support Vector Machine using minimum features of GLCM. The proposed steps are started by converting image to grayscale and extracting colour feature using four features of GLCM. The features include Energy, Entropy, Contras, Correlation and 0o, 45o, 90o, and 135o. The classifier features consist of 16 features in total. In the experimental result, there exist comparison of previous works regarding the classification KNN and SVM using multi texton histogram (MTH). The experiments are carried out in the form of calculation of accuracy with data sharing and cross-validation scenario. From the test results, the average accuracy for KNN is 78.3% and 92.3% for SVM in the cross-validation scenario. The scenario for the highest accuracy of data sharing is at 70% for KNN and at 100% for SVM. Thus, it is apparent that the application of the GLCM and SVM method for extracting and classifying batik motifs has been effective and better than previous work.

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Section: Gray Level Co-occurence Matrix (Glcm)mentioning

confidence: 99%

Classification of batik patterns using K-Nearest neighbor and support vector machine

et al. 2020

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“…GLCM is a first and second-order statistical method used by Haralick to extract texture features from an image [23]. The GLCM method used in this second test refers to Minarno's research which produces four values, namely entropy, correlation, energy, and contrast [24] [25]. Table 2 shows the results of the comparison performed.…”

Section: Datasetmentioning

confidence: 99%

Image Retrieval Based on Texton Frequency-Inverse Image Frequency

Azhar

Minarno

Munarko

et al. 2020

KINETIK

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In image retrieval, the user hopes to find the desired image by entering another image as a query. In this paper, the approach used to find similarities between images is feature weighting, where between one feature with another feature has a different weight. Likewise, the same features in different images may have different weights. This approach is similar to the term weighting model that usually implemented in document retrieval, where the system will search for keywords from each document and then give different weights to each keyword. In this research, the method of weighting the TF-IIF (Texton Frequency-Inverse Image Frequency) method proposed, this method will extract critical features in an image based on the frequency of the appearance of texton in an image, and the appearance of the texton in another image. That is, the more often a texton appears in an image, and the less texton appears in another image, the higher the weight. The results obtained indicate that the proposed method can increase the value of precision by 7% compared to the previous method.

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“…The performance of ICRLBP is measured using precision and recall which are defined in equation (10) and (11), respectively.…”

Section: Performance Measurementioning

confidence: 99%

Efficient texture image retrieval of improved completed robust local binary pattern

Kurniawardhani

Minarno

Bimantoro

2016

2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS)

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Improved Completed Robust Local Binary Pattern is one of the robust texture extraction for image retrieval that rotation invariant (ICRLBP). ICRLBP has proven that can increase the precision, recall, and computation time from its previous work by 21.14%, 20.03%, and 56 times, respectively, on four different texture image dataset. ICRLBP, however, has a lot of feature, thus require more time during recognition process. Moreover, it leads to high time consuming and curse of dimensionality. To overcome those issues, in this paper, we try to reduce insignificant or unnecessary ICRLBP attributes and examine the effect of reducing number of attributes on precision and recall of the retrieving images. The methods we used to reduce the ICRLBP attributes are Correlation-based Feature Selection (CFS) and Pearson's-basedCorrelation.The experiment results show that those feature selections not only can reduce number of attributes but also can improve precision, recall, and computation time. For S_M_C feature (sign, magnitude, and center features of ICRLBP are ploted on histogram jointly), CFS can reduce up to 95% number of attributes and improve precision, recall, computation time up to 7.5%, 7.1%, 11.42 times, respectively. For M_C feature (magnitude and center features of ICRLBP are ploted on histogram jointly), CFS can reduce up to 4.2% number of attributes and improve precision, recall, computation time up to 4.2%, 4%, 1.1 times, respectively. It indicated that Correlation-based Feature Selection (CFS) and Pearson's-basedCorrelation can reduce the ICRLBP attributes effectively.

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Image Retrieval Based on Multi Structure Co-occurrence Descriptor

Abstract: This study presents a new technique for Batik cloth image retrieval using

Cited by 9 publications

References 6 publications

Classification of batik patterns using K-Nearest neighbor and support vector machine

Classification of batik patterns using K-Nearest neighbor and support vector machine

Image Retrieval Based on Texton Frequency-Inverse Image Frequency

Efficient texture image retrieval of improved completed robust local binary pattern

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