Fast discrete curvelet transform and HSV color features for batik image clansificotlon

Suciati, Nanik; Kridanto, Agri; Naufal, Mohammad Farid; Machmud, Muhammad; Wicaksono, Ardian Yusuf

doi:10.1109/icts.2015.7379879

Cited by 16 publications

(17 citation statements)

References 8 publications

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“…In this study used a dataset previously used in Wang's research [12] in his study entitled Learning Models for Object Recognition from Natural Language Descriptions. At this stage the process of collecting and analyzing data to be used as a dataset.…”

Section: Methodsmentioning

confidence: 99%

“…Figure 1a shows the original image used as the dataset. Figure 1b is a mask of previous research [12], which is used for cropping data to obtain Fig. 1c.…”

Section: Methodsmentioning

confidence: 99%

“…After the noise on the image is removed, then it is normalized to the dataset, i.e. resizing the image ant it is necessary the output data has the same size [9]. In our case, all image sizes on the dataset will be normalized to the same size, which is 420x315 pixels.…”

Section: Methodsmentioning

confidence: 99%

“…The study used koi fish dataset which will be classified. In this study mentioned the basic components commonly used for research in the field of the image among other features of color, shape, and texture [9]. In Satria research related classification based on color in koi fish get an accuracy value of 97.92%.…”

Section: Introductionmentioning

confidence: 99%

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Local Binary Pattern Method and Feature Shape Extraction for Detecting Butterfly Image

Kartika¹

2018

GEOMATE

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ABSTRACT:Research in the field of information retrieval especially on image processing is proliferating. Various methods are developed to be able to detect images optimally and produce better accuracy. The process of image detection can use the dataset that exists around us. In this research, we use butterflies dataset, since the butterfly has unique colors, patterns, and diverse shapes. Therefore, we use local binary pattern method for texture feature extraction and region props for shape feature extraction. The results of each texture feature extraction and shape feature extraction will be a merging process. The results of the merging process get an accuracy of 66%. In addition, the system testing process with confusion matrix will produce 67.1% precision value, 66% recall and f-measure of 66.5%. The merging process of both methods shows the interplay of texture and shape extraction.

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Section: Methodsmentioning

confidence: 99%

“…Figure 1a shows the original image used as the dataset. Figure 1b is a mask of previous research [12], which is used for cropping data to obtain Fig. 1c.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Local Binary Pattern Method and Feature Shape Extraction for Detecting Butterfly Image

Kartika¹

2018

GEOMATE

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“…Research of batik image classification has previously been done by [2] using Curvelet Transformation, HSV color space, and k-Nearest Neighbor (KNN) classifier. Researcher [3] also did a comparison method for batik classification using GLCM, Canny edge detection, and Gabor filter.…”

mentioning

confidence: 99%

Modified Convolutional Neural Network Architecture for Batik Motif Image Classification

Wicaksono

Suciati

Fatichah

et al. 2017

IJOS

Self Cite

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AbstractBatik is one of the cultural heritages of Indonesia that have many different motifs in each region as well as in its usage. However, the Indonesians sometimes not knowing the batik motif that they're wearing every day, and sometimes they have a batik image without knowing batik information contained in their batik image. With the growing number of images of batik and batik motifs, a classification method that can classify various motifs of batik is required to automatically detect the motif from the batik image. Image processing using the Deep Learning especially for image classification is widely used recently because it has good results. The most popular method in deep learning is Convolutional Neural Network (CNN) which has been proved robust in natural images. This study offers a batik motif image classification system using CNN method with new network architecture developed by combining GoogLeNet and Residual Networks named IncRes. IncRes merges the Inception Module with Residual Network structure. With the 70.84% accuracy, the system can be used to classify the batik image motif accurately.

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Privacy preserving content based image retrieval

Kayani,

Riaz,

Ghafoor

et al. 2023

Multimed Tools Appl

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Fast discrete curvelet transform and HSV color features for batik image clansificotlon

Cited by 16 publications

References 8 publications

Local Binary Pattern Method and Feature Shape Extraction for Detecting Butterfly Image

Local Binary Pattern Method and Feature Shape Extraction for Detecting Butterfly Image

Modified Convolutional Neural Network Architecture for Batik Motif Image Classification

Privacy preserving content based image retrieval

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