Batik Image Classification Using SIFT Feature Extraction, Bag of Features and Support Vector Machine

Azhar, Ryfial; Tuwohingide, Desmin; Kamudi, Dasrit Debora; Sarimuddin,; Suciati, Nanik

doi:10.1016/j.procs.2015.12.101

Cited by 91 publications

(42 citation statements)

References 12 publications

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“…Table 2 shows the confusion matrix representation of the results obtained by our proposed method. From these values accuracy can also be calculated using (11). Our proposed approach classify the image database considering the features extracted from the images.…”

Section: Experimental Observations and Discussionmentioning

confidence: 99%

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A Histogram-based Classification of Image Database Using Scale Invariant Features

Mishra¹,

Panda²

2017

IJIGSP

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Abstract-Development of advanced technology has increased the size of data and has also created different categories of data. Classifying these different categories of data is the need of the era. We have proposed a method of classifying the image database containing four categories of images like human face, airplane, cup and butterfly. Our approach involves steps like feature extraction, bag of feature creation, histogram representation and classification using decision tree. For feature extraction SIFT (Scale Invariant Feature Transform) algorithm is used since it is invariant to rotation, change of scale, illumination etc. After extracting the features the bag of features concept is used to group the features using k-means clustering algorithm. Then a histogram is plotted for each image in the image database which represents the distributions of data in different clusters. In the final step the most robust, simple and flexible decision tree algorithm is applied on the table created from the histogram plots to obtain the classification result. The experimental observations and the calculated accuracy proves that this method of classification works well for classifying an image dataset having different categories of images.

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Section: Experimental Observations and Discussionmentioning

confidence: 99%

“…Lu and D. Yang [10] proposed a decision tree based image mining technique. They extracted pixel-wise image Batik images (a traditional fabric of Indonesia) were classified by extracting features using SIFT (Scale Invariant Feature Transform), constructing bag of features and using SVM (Support Vector Machine) for classification [11].…”

Section: Introductionmentioning

confidence: 99%

A Histogram-based Classification of Image Database Using Scale Invariant Features

Mishra¹,

Panda²

2017

IJIGSP

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“…Additionally, R.A zhar, et al [14][15] used a combination of Bag of Features (BOF) using Scale Invariant Feature Transform (SIFT) and Support Vector Machine (SVM ) to classify the batik image. This experiment produced a classification accuracy of 97.67% for regular batik image, 95.47% fo r the rotated image and 79% for the scaled image.…”

Section: Introductionmentioning

confidence: 99%

Batik Classification with Artificial Neural Network Based on Texture-Shape Feature of Main Ornament

Kasim¹,

Wardoyo²,

Harjoko³

2017

IJISA

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Abstract-Batik is a text ile with mot ifs of Indonesian culture wh ich has been recognized by UNESCO as world cultural heritage. Bat ik has many motifs which are classified in various classes of batik. This study aims to combine the features of texture and the feature of shapes' ornament in batik to classify images using artificial neural networks. The value of texture features of images in batik is extracted using a gray level co-occurrence matrices (GLCM) wh ich include Angular Second Moment (ASM) / energy), contrast, correlation, and inverse different mo ment (IDM ). The value of shape features is extracted using a binary mo rphological operation which includes compactness, eccentricity, rectangularity and solidity. At this phase of the training and testing, we compare the value of a classification accuracy of neural networks in each class in batik with their texture features, their shape, and the combination of texture and shape features. From the three features used in the classification of batik image with artificial neural networks, it was obtained that shape feature has the lowest accuracy rate of 80.95% and the combination of texture and shape features produces a greater value of accuracy by 90.48%. The results obtained in this study indicate that there is an increase in accuracy of batik image classification using the artificial neural network with the co mb ination of texture and shape features in batik image.

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“…Feature extraction and SVM (Support Vector Machines) have been frequently used in many fields of research such as energy management systems [2], watermarking [3], disease diagnosis [4,5], industrial materials [6,7], image [8] and signal processing [9] etc.…”

Section: Introductionmentioning

confidence: 99%

Seam carving based image resizing detection using hybrid features

Şentürk

Akgün

2017

Teh. vjesn.

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Otkrivanje promjene veličine slike temeljeno na tekućem skaliranju uporabom hibridnih značajkiTehnički vjesnik 24, 6(2017), 1825-1832 1825 ISSN 1330, ISSN 1848-6339 (Online) https://doi.org/10.17559/TV-20160804121351 SEAM CARVING BASED IMAGE RESIZING DETECTION USING HYBRID FEATURES Zehra Karapinar Senturk, Devrim AkgunOriginal scientific paper Detection of seam carving-based digital image resizing is a challenging task in image processing field since the method investigates the images on hand semantically. Resizing with seam carving is realized by inserting or removing relatively unimportant pixel paths to/from the images and so the changes in image content are mostly unnoticeable. Local Binary Patterns (LBP), a visual descriptor, unearths local changes in image texture. Therefore, using LBP transform of the images besides intensity values contributes to the detection ratio. In this paper, we proposed a hybrid detection mechanism for more accurate seam carving detection especially in low scaling ratios. We extracted LBP-based and non-LBP based features and trained a Support Vector Machine (SVM) with sixty features. We achieved approximately 9 % improvement in low detection ratios. The experimental results show that more satisfactory detection ratios can be obtained by the proposed hybrid approach. Keywords: forgery detection; Local Binary Patterns; seam carving; Support Vector Machines Otkrivanje promjene veličine slike temeljeno na tekućem skaliranju uporabom hibridnih značajkiIzvorni znanstveni članak Otkrivanje promjene veličine digitalnih slika temeljeno na tekućem skaliranju je izazovna zadaća u području obrade slike budući da metoda istražuje slike na slici semantički. Promjena veličine s tekućim skaliranjem ostvaruje se umetanjem na slike ili uklanjanjem sa slika relativno nevažnih puteva piksela, zbog čega se promjene u slikovnom sadržaju uglavnom ne mogu primijetiti. Lokalni binarni uzorci (LBP), vizualni deskriptor, otkrivaju lokalne promjene u teksturi slike. Stoga, uporabom LBP transformacije slika osim vrijednosti intenziteta doprinosi se omjeru otkrivanja. U ovom smo radu predložili hibridni mehanizam otkrivanja za točnije otkrivanje tekućeg skaliranja, posebice u niskim omjerima skaliranja. Izdvojili smo značajke temeljene i netemeljene na LBP-u te sa šezdeset značajki vježbali podrškom vektorskih strojeva (SVM). Postigli smo približno 9 % poboljšanja u omjerima niskog otkrivanja. Pokusni rezultati pokazuju da se zadovoljavajući omjeri otkrivanja mogu dobiti predloženim hibridnim pristupom.Ključne riječi: lokalni binarni uzorci; otkrivanje krivotvorenja; podrška vektorskih strojeva; tekuće skaliranje (seam carving)

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Batik Image Classification Using SIFT Feature Extraction, Bag of Features and Support Vector Machine

Cited by 91 publications

References 12 publications

A Histogram-based Classification of Image Database Using Scale Invariant Features

A Histogram-based Classification of Image Database Using Scale Invariant Features

Batik Classification with Artificial Neural Network Based on Texture-Shape Feature of Main Ornament

Seam carving based image resizing detection using hybrid features

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