Effect of data-augmentation on fine-tuned CNN model performance

Poojary, Ramaprasad; Raina, Roma; Mondal, Amit Kumar

doi:10.11591/ijai.v10.i1.pp84-92

Cited by 36 publications

(20 citation statements)

References 16 publications

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“…The Dunhuang mural image data set is divided into the training set (80%) and the test set (20%). The data augmentation technology of Keras is used to reduce the impact of small-scale training sets on model training (Poojary et al , 2021). Then, the learning rate is set to 1.0 × 10^−4, and the loss function is set to binary cross-entropy.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Constructing a mobile visual search framework for Dunhuang murals based on fine-tuned CNN and ontology semantic distance

Zeng

Sun

et al. 2022

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Purpose Dunhuang murals are rich in cultural and artistic value. The purpose of this paper is to construct a novel mobile visual search (MVS) framework for Dunhuang murals, enabling users to efficiently search for similar, relevant and diversified images. Design/methodology/approach The convolutional neural network (CNN) model is fine-tuned in the data set of Dunhuang murals. Image features are extracted through the fine-tuned CNN model, and the similarities between different candidate images and the query image are calculated by the dot product. Then, the candidate images are sorted by similarity, and semantic labels are extracted from the most similar image. Ontology semantic distance (OSD) is proposed to match relevant images using semantic labels. Furthermore, the improved DivScore is introduced to diversify search results. Findings The results illustrate that the fine-tuned ResNet152 is the best choice to search for similar images at the visual feature level, and OSD is the effective method to search for the relevant images at the semantic level. After re-ranking based on DivScore, the diversification of search results is improved. Originality/value This study collects and builds the Dunhuang mural data set and proposes an effective MVS framework for Dunhuang murals to protect and inherit Dunhuang cultural heritage. Similar, relevant and diversified Dunhuang murals are searched to meet different demands.

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Section: Experiments and Resultsmentioning

confidence: 99%

Constructing a mobile visual search framework for Dunhuang murals based on fine-tuned CNN and ontology semantic distance

Zeng

Sun

et al. 2022

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“…From Phellan et al [12] experiment, it is proved that as a greater number of images used will increase the training times and the time taking for testing are not depending on the quantity of training images and the segmentation accuracy and cannot be increased by more training images. Figure 4 shows the segmentation results using the deep CNN [21]. Figure 4(a) shows the result obtained manually and Figure 4(b) depicts the CNN approach output.…”

Section: Resultsmentioning

confidence: 99%

Automatic cerebrovascular segmentation methods-a review

Taher

Prakash

2021

IJ-AI

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Cerebrovascular diseases are one of the serious causes for the increase in mortality rate in the world which affect the blood vessels and blood supply to the brain. In order, diagnose and study the abnormalities in the cerebrovascular system, accurate segmentation methods can be used. The shape, direction and distribution of blood vessels can be studied using automatic segmentation. This will help the doctors to envisage the cerebrovascular system. Due to the complex shape and topology, automatic segmentation is still a challenge to the clinicians. In this paper, some of the latest approaches used for segmentation of magnetic resonance angiography images are explained. Some of such methods are deep convolutional neural network (CNN), 3dimentional-CNN (3D-CNN) and 3D U-Net. Finally, these methods are compared for evaluating their performance. 3D U-Net is the better performer among the described methods.

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“…We apply augmentation to reduce overfitting during training. It's about enlarging the data set by producing a slight distortion (transformation) to the images: rotation, translation, shearing, scaling and reflection [33]. Thus, we obtain the longer database Table 1.…”

Section: Augmentation Processmentioning

confidence: 99%

Low-cost convolutional neural network for tomato plant diseases classifiation

Bensaadi

Louchene

2023

IJ-AI

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<span lang="EN-US">Agriculture is a crucial element to build a strong economy, not only because of its importance in providing food, but also as a source of raw materials for industry as well as source of energy. Different diseases affect plants, which leads to decrease in productivity. In recent years, developments in computing technology and machine-learning algorithms (such as deep neural networks) in the field of agriculture have played a great role to face this problem by building early detection tools. In this paper, we propose an automatic plant disease classification based on a low complexity convolutional neural network (CNN) architecture, which leads to faster on-line classification. For the training process, we used more than one 57.000 tomato leaf images representing nine classes, taken under natural environment, and considered during training without background subtraction. The designed model achieves 97.04% classification accuracy and less than 0.2 error, which shows a high accuracy in distinguishing a disease from another.</span>

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Effect of data-augmentation on fine-tuned CNN model performance

Cited by 36 publications

References 16 publications

Constructing a mobile visual search framework for Dunhuang murals based on fine-tuned CNN and ontology semantic distance

Constructing a mobile visual search framework for Dunhuang murals based on fine-tuned CNN and ontology semantic distance

Automatic cerebrovascular segmentation methods-a review

Low-cost convolutional neural network for tomato plant diseases classifiation

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