Convolutional neural network for color images classification

Mohammed, Nora; Abed, Mohammed Hamzah; Albu-Salih, Alaa Taima

doi:10.11591/eei.v11i3.3730

Cited by 7 publications

(5 citation statements)

References 20 publications

(27 reference statements)

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“…According to earlier research, it is possible to input an image directly into a CNN network and use features for image classification [30]. The basic CNN architecture is composed of convolutional layers, pooling layers, fully connected layers (FC), SoftMax layers, and non-linear activation functions like rectifier neural network (ReLU) [31], [32]. The forward pass stage includes a convolution layer, where an activation map is produced as the result of computing the dot product of the filter's input volume and the filter's dot product.…”

Section: Methodsmentioning

confidence: 99%

Content-based image retrieval based on corel dataset using deep learning

Hassan

Sultani

Dhannoon

2023

IJ-AI

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A popular technique for retrieving images from huge and unlabeled image databases are content-based-image-retrieval (CBIR). However, the traditional information retrieval techniques do not satisfy users in terms of time consumption and accuracy. Additionally, the number of images accessible to users are growing due to web development and transmission networks. As the result, huge digital image creation occurs in many places. Therefore, quick access to these huge image databases and retrieving images like a query image from these huge image collections provides significant challenges and the need for an effective technique. Feature extraction and similarity measurement are important for the performance of a CBIR technique. This work proposes a simple but efficient deep-learning framework based on convolutional-neural networks (CNN) for the feature extraction phase in CBIR. The proposed CNN aims to reduce the semantic gap between low-level and high-level features. The similarity measurements are used to compute the distance between the query and database image features. When retrieving the first 10 pictures, an experiment on the Corel-1K dataset showed that the average precision was 0.88 with Euclidean distance, which was a big step up from the state-of-the-art approaches.

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Section: Methodsmentioning

confidence: 99%

Content-based image retrieval based on corel dataset using deep learning

Hassan

Sultani

Dhannoon

2023

IJ-AI

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“…𝑧 𝑖,𝑗 ℎ = ∑ ∑ 𝑎 (𝑖−1)𝑠+𝑚,(𝑗−1)𝑠+𝑛 * 𝑘 𝑚,𝑛 + 𝑏 ℎ 𝑁 𝑛=1 𝑀 𝑚=1 (5) where 𝑧 𝑖,𝑗 ℎ is the output of the convolutional operation with 𝑖-th row, 𝑗-th column, and ℎ-th channel. 𝑏 ℎ is the bias of the ℎ-th channel.…”

Section: Figure 2 Convolutional Neural Network (Cnn)mentioning

confidence: 99%

“…In the context of image classification, DL methods are often the first choice [4]. Another one of the most popular DL methods used for many image processing and human health applications is the convolutional neural network [5]. Convolutional neural network (CNN) is a machine learning algorithm that has shown to make good results for image classifications [6].…”

Section: -Introductionmentioning

confidence: 99%

Comparison of Activation Functions in Convolutional Neural Network for Poisson Noisy Image Classification

Goh,

Surono,

Afiatin

et al. 2024

Emerg Sci J

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Deep learning, specifically the Convolutional Neural Network (CNN), has been a significant technology tool for image processing and human health. CNNs, which mimic the working principles of the human brain, can learn robust representations of images. However, CNNs are susceptible to noise interference, which can impact classification performance. Choosing the right activation function can improve CNNs performance and accuracy. This research aims to test the accuracy of CNN with ResNet50, VGG16, and GoogleNet architectures combined with several activation functions such as ReLU, Leaky ReLU, Sigmoid, and Tanh in the classification of images that experience Poisson noise. Poisson noise is applied to each test data to evaluate CNN accuracy. The data used in this study consists of three scenarios of different numbers of classes, namely 3 classes, 5 classes, and 10 classes. The results showed that combining ResNet50 with the ReLU activation function produced the best performance in class recognition in each scenario of the number of classes experiencing Poisson noise interference. The model achieved 97% accuracy for 3-class data, 95% for 5-class data, and 90% for 10-class data. These results show that using ResNet50 with the ReLU activation function can provide excellent resistance to Poisson noise in image processing. It was found that as the number of classes increases, the accuracy of image recognition tends to decrease. This shows that the more complex the image classification task is with a larger number of classes, the more difficult it is for CNNs to distinguish between different classes. Doi: 10.28991/ESJ-2024-08-02-014 Full Text: PDF

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“…The kernel size can vary, but 3×3 or 5×5 matrices are commonly used. Following the convolution layer, the rectified linear unit (ReLU), which is frequently used in neural networks, performs the nonlinear activation process [24].…”

Section: Convolutional Layermentioning

confidence: 99%

PithaNet: A transfer learning-based approach for traditional pitha classification

Shakil,

Akash,

Nabi

et al. 2023

IJECE

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<span lang="EN-US">Pitha, pithe, or peetha are all Bangla words referring to a native and traditional food of Bangladesh as well as some areas of India, especially the parts of India where Bangla is the primary language. Numerous types of pithas exist in the culture and heritage of the Bengali and Bangladeshi people. Pithas are traditionally prepared and offered on important occasions in Bangladesh, such as welcoming a bride grooms, or bride, entertaining guests, or planning a special gathering of family, relatives, or friends. The traditional pitha celebration and pitha culture are no longer widely practiced in modern civilization. Consequently, the younger generation is unfamiliar with our traditional pitha culture. In this study, an effective pitha image classification system is introduced. convolutional neural network (CNN) pre-trained models EfficientNetB6, ResNet50, and VGG16 are used to classify the images of pitha. The dataset of traditional popular pithas is collected from different parts of Bangladesh. In this experiment, EfficientNetB6 and ResNet50 show nearly 90% accuracy. The best classification result was obtained using VGG16 with 92% accuracy. The main motive of this study is to revive the Bengali pitha tradition among young people and people worldwide, which will encourage many other researchers to pursue research in this domain.</span>

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Convolutional neural network for color images classification

Cited by 7 publications

References 20 publications

Content-based image retrieval based on corel dataset using deep learning

Content-based image retrieval based on corel dataset using deep learning

Comparison of Activation Functions in Convolutional Neural Network for Poisson Noisy Image Classification

PithaNet: A transfer learning-based approach for traditional pitha classification

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