Convolutional Neural Networks: An Overview and Its Applications in Pattern Recognition

Patil, Aseem; Rane, Milind

doi:10.1007/978-981-15-7078-0_3

Cited by 100 publications

(59 citation statements)

References 3 publications

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“…As one layer feeds its output into the next layer, extracted features can hierarchically and progressively become more complex. The process of optimizing parameters such as kernels is called training, which is performed so as to minimize the difference between outputs and ground truth labels through an optimization algorithm called backpropagation and gradient descent, among others [14].…”

Section: Figure 1 Cnn Methods Layer Sequencesmentioning

confidence: 99%

Application of Deep Learning Using Convolutional Neural Network (CNN) Method For Women’s Skin Classification

Anton

Nissa

Janiati

et al. 2021

SJI

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Facial skin is skin that protects the inside of the face such as the eyes, nose, mouth, and others. Facial skin consists of several types, including normal skin, oily skin, dry skin, and combination skin. This is a problem for women because it is difficult to recognize and distinguish their skin types this is what causes some women to find it difficult to determine the right make-up and care products for their skin types. In this study, the Convolutional Neural Network (CNN) method is the right method for classifying women's skin types from the age of 20-30 years by following several stages using Python 3.5 programming with a depth of three layers and the results of this research using the CNN method get the results of the accuracy value good at 67%

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Section: Figure 1 Cnn Methods Layer Sequencesmentioning

confidence: 99%

Application of Deep Learning Using Convolutional Neural Network (CNN) Method For Women’s Skin Classification

Anton

Nissa

Janiati

et al. 2021

SJI

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“…CNN relays on labeled data (Voulodimos et al, 2018), which is considered one of its limitations. Other difference is that CNNs do not demand hand-craft feature extraction (Patil & Rane, 2021). Table 3 clarifies the main characteristics, limitations and examples for the main machine learning categories.…”

Section: Deep Learningmentioning

confidence: 99%

Content-based image retrieval: A review of recent trends

2021

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With the availability of internet technology and the low-cost of digital image sensor, enormous amount of image databases have been created in different kind of applications. These image databases increase the demand to develop efficient image retrieval search methods that meet user requirements. Great attention and efforts have been devoted to improve content-based image retrieval method with a particular focus on reducing the semantic gap between low-level features and human visual perceptions. Due to the increasing research in this field, this paper surveys, analyses and compares the current state-of-the-art methodologies over the last six years in the CBIR field. This paper also provides an overview of CBIR framework, recent low-level feature extraction methods, machine learning algorithms, similarity measures, and a performance evaluation to inspire further research efforts.

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“…Machine vision/computer vision, known as a subset of artificial intelligence (AI), is mainly used for problems with image and video recognition, image analysis and classification, media recreation, recommendation systems, natural language processing, etc. [ 33 , 34 , 35 , 36 ]. Convolutional neural networks (CNN) are a deep-learning-based robust algorithm to fulfill machine vision tasks.…”

Section: An Overview Of Software Used To Analyze An Image or Videomentioning

confidence: 99%

“…One or more fully connected layers, also called dense layers, connect every input to every output by a learnable weight. The whole process is explained as features from an image/video extracted in the convolution layer, and then it is down sampled by pooling layers; they are then mapped by a subset of fully connected layers to create the final outputs of the network, such as the probabilities for each class in classification tasks [ 34 , 35 , 36 ].…”

Section: An Overview Of Software Used To Analyze An Image or Videomentioning

confidence: 99%

“…However, pre-trained CNN models are ready to use based on the particular problem. Unlike other CNN models, pre-trained models do not need to be developed from scratch since they were developed and trained using a considerable number of public and private datasets to be applicable to a particular problem [ 33 , 35 , 36 ]. For instance, GoogLeNet is an inception-based pre-trained model developed to classify more than 1000 objects and was trained with a large number of class object images.…”

Section: An Overview Of Software Used To Analyze An Image or Videomentioning

confidence: 99%

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The Application of Cameras in Precision Pig Farming: An Overview for Swine-Keeping Professionals

Arulmozhi

Bhujel

Moon

et al. 2021

Animals

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Pork is the meat with the second-largest overall consumption, and chicken, pork, and beef together account for 92% of global meat production. Therefore, it is necessary to adopt more progressive methodologies such as precision livestock farming (PLF) rather than conventional methods to improve production. In recent years, image-based studies have become an efficient solution in various fields such as navigation for unmanned vehicles, human–machine-based systems, agricultural surveying, livestock, etc. So far, several studies have been conducted to identify, track, and classify the behaviors of pigs and achieve early detection of disease, using 2D/3D cameras. This review describes the state of the art in 3D imaging systems (i.e., depth sensors and time-of-flight cameras), along with 2D cameras, for effectively identifying pig behaviors and presents automated approaches for the monitoring and investigation of pigs’ feeding, drinking, lying, locomotion, aggressive, and reproductive behaviors.

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Convolutional Neural Networks: An Overview and Its Applications in Pattern Recognition

Cited by 100 publications

References 3 publications

Application of Deep Learning Using Convolutional Neural Network (CNN) Method For Women’s Skin Classification

Application of Deep Learning Using Convolutional Neural Network (CNN) Method For Women’s Skin Classification

Content-based image retrieval: A review of recent trends

The Application of Cameras in Precision Pig Farming: An Overview for Swine-Keeping Professionals

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