Enhancement of traditional clothes pattern recognation using Convolutional Neural Network

Rangkuti, Abdul Haris; Athala, Varyl Hasbi; Luthfi, Naufal Fauzi; Aditama, Syaugi Vikri; Ramadhan, Muhammad Maulana; Aslamia, Ayu Hidayah

doi:10.1109/icoco53166.2021.9673549

Cited by 5 publications

(5 citation statements)

References 5 publications

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“…[15] Another research on the classification of traditional cloth patterns that experience changes in rotation and scale using several Convolutional neural network models [16]. The average resulting classification accuracy of up to more than 75% with a total of 44 types of traditional cloth patterns [17]. Other research related to patterns resembling animal skin patterns is on Batik cloth patterns, where mulwin-LBP and Deep neural networks are used for feature extraction.…”

Section: Methodsmentioning

confidence: 99%

A Novel Approach of Animal Skin Classification Using CNN Model with CLAHE and SUCK Method Support

Rangkuti,

Hasbi

2023

JOIV : Int. J. Inform. Visualization

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This study describes the process of classifying animal skin images which are rather difficult to obtain optimal image characteristics. For this reason, in the pre-processing stage, we propose two methods to support feature extraction: sharpening using a convolutional kernel (SUCK-Sharpening) and adaptive histogram equalization with limited contrast (CLAHE-Equalized). SUCK works by operating on these pixel values using direct math to build a new image; this final value is the new value of the current pixel. CLAHE overcomes the limitations of the global approach by performing local contrast enhancement. Because of the advantages of the two methods, it becomes a solution to get features processed at the feature extraction and classification stage. The process of animal skin imagery has characteristics in terms of shape and texture, including the characteristics of animal skin color. In this study, some experiments have been carried out on several CNN models, with an average classification accuracy of more than 70% using the sharpened and equalized methods on six animal skins. More detail, the average classification accuracy using 3 CNN models supported by two methods, namely Sharpening and Equalize on the CNN Resnet 50V2 model is 67.73% and 73.78%, InceptionV3 model at 82.13%, and 74.76% and Densenet121 models were 87.64% and 87.46 %. This research can be continued to improve the accuracy of other animal skin images, including determining fake or genuine skin images.

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Section: Methodsmentioning

confidence: 99%

A Novel Approach of Animal Skin Classification Using CNN Model with CLAHE and SUCK Method Support

Rangkuti,

Hasbi

2023

JOIV : Int. J. Inform. Visualization

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“…A study found that by using a GPU as the main processing power, they achieved a 177x acceleration on training data and a 175x acceleration on test data [14]. To ensure the reliability of the dataset used, the researchers utilized the fruis-360 dataset, which had already been successfully used in previous research [15]. This dataset includes photos of 30 different fruit classes.…”

Section: Related Studymentioning

confidence: 99%

Comparative Analysis of Banana Detection Models

Haris Rangkuti,

Hasbi Athala,

Lun Lau

et al. 2024

Int. j. electr. comput. eng. syst. (Online)

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This study aims to compare and evaluate the performance of banana detection models utilizing deep learning techniques and the Darknet algorithm. The objective is to identify the most effective approach for accurately detecting bananas in various real- world scenarios. The analysis involves training and testing multiple models using different datasets and evaluating their performance based on precision, recall, and overall accuracy. The results provide valuable insights into the strengths and weaknesses of each approach, enabling researchers and practitioners to make informed decisions when implementing banana detection systems. To detect banana objects, several convolutional neural network (CNN) models were used, including MobileNetV2, YOLOv3-Nano, YOLO Fastest 1.1, YOLOv3-tiny-PRN, YOLOv4-tiny, YOLOv7, and DenseNet121-YOLOv3. The training process utilizes the Darknet algorithm to facilitate the identification of banana types/classes captured by a camera, resulting in an MP4 film file. In this research, various experiments were carried out using different CNN models. However, these six models achieve optimal accuracy above 80%. Among them, the YOLOv7 model shows the highest average accuracy (MAP) at 100%, followed by the small model YOLOv4 at 92%. Meanwhile, for performance measurements, the accuracy of the YOLOv4-tiny model was 87%, followed by the YOLOv7 model at 84%. In the banana fruit experiment, several models showed very good performance, such as recognition of the Ambon, Kepok, and Emas banana classes up to 100% using the YOLOv7 and YOLOv4-tiny models. The YOLOv7 model itself can recognize other banana classes up to 100% in the Barangan, Rjbulu, Uli, and Tanduk classes. Furthermore, theYOLOv4-tiny model can recognize other banana classes, up to 90% of the Barangan, Rjbulu, Rjsereh, and Uli banana types. Thus, this experiment provides very good average accuracy results on 2 CNN models, namely YOLOv7 and YOLOv4-tiny. Future research will involve grouping pictures of bananas, which produces different image shapes, so it requires a different way to recognize them. It is hoped that this research can become a basis for further research in this field.

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“…The structure of MobileNetV2 can be seen in Fig 3 . MobileNetV2 has been tested on ImageNet classification. In addition, MobileNetV2 was also tested on pattern recognition of traditional clothes [24] and batik image classification [25]. The MobileNetV2 model uses a convolution block with a unique property that separates the model network's expressiveness capacity by using an input bottleneck [26].…”

Section: Modelsmentioning

confidence: 99%

Batik Images Retrieval Using Pre-trained model and K-Nearest Neighbor

Minarno

Hasanuddin

Azhar

2023

JOIV : Int. J. Inform. Visualization

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Batik is an Indonesian cultural heritage that should be preserved. Over time, many batik motifs have sprung up, which can lead to mutual claims between craftsmen. Therefore, it is necessary to create a system to measure the similarity of a batik motif. This research is focused on making Content-Based Image Retrieval (CBIR) on batik images. The dataset used in this research is big data Batik images. The authors used transfer learning on several pre-trained models and used Convolutional Neural Network (CNN) Autoencoder from previous studies to extract features on all images in the database. The extracted features calculate the Euclidean distance between the query and all images in the database to retrieve images. The image closest to the query will be retrieved according to the number of r, namely 3, 5, 10, or 15. Before the image is retrieved, the retrieval system is used to re-ranked with K-Nearest Neighbor (KNN), which classifies the retrieved image. The results of this study prove that MobileNetV2 + KNN is the best model in terms of Image Retrieval Batik, followed by InceptionV3 and VGG19 as the second and third ranks. Moreover, CNN Autoencoder from previous research and InceptionResNetV2 are ranked fourth and fifth. In this study, it was also found that the use of KNN re-ranking can increase the precision value by 0.00272. For further research, deploying these models, especially for MobileNetV2 is an approach for seeing a major impact on batik craftsmanship for decreasing batik motif plagiarism.

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Enhancement of traditional clothes pattern recognation using Convolutional Neural Network

Cited by 5 publications

References 5 publications

A Novel Approach of Animal Skin Classification Using CNN Model with CLAHE and SUCK Method Support

A Novel Approach of Animal Skin Classification Using CNN Model with CLAHE and SUCK Method Support

Comparative Analysis of Banana Detection Models

Batik Images Retrieval Using Pre-trained model and K-Nearest Neighbor

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