A Genetic Programming Approach to Integrate Multilayer CNN Features for Image Classification

Chu, Wei-Ta; Chu, Hao

doi:10.1007/978-3-030-05710-7_53

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…The feature-learning based methods for the HAR task can further be grouped into (1) Traditional and, (2) Deep-learning-based methods. i) Traditional approaches: This includes methods like genetic programming [36,100], dictionary learning [110,226] and Bayesian networks [92,101].…”

Section: Har Approaches According To Feature Extraction Processmentioning

confidence: 99%

Vision-based human activity recognition: a survey

Beddiar

Nini

Sabokrou

et al. 2020

Multimed Tools Appl

330

117

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Human activity recognition (HAR) systems attempt to automatically identify and analyze human activities using acquired information from various types of sensors. Although several extensive review papers have already been published in the general HAR topics, the growing technologies in the field as well as the multi-disciplinary nature of HAR prompt the need for constant updates in the field. In this respect, this paper attempts to review and summarize the progress of HAR systems from the computer vision perspective. Indeed, most computer vision applications such as human computer interaction, virtual reality, security, video surveillance and home monitoring are highly correlated to HAR tasks. This establishes new trend and milestone in the development cycle of HAR systems. Therefore, the current survey aims to provide the reader with an up to date analysis of vision-based HAR related literature and recent progress in the field. At the same time, it will highlight the main challenges and future directions.

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Section: Har Approaches According To Feature Extraction Processmentioning

confidence: 99%

Vision-based human activity recognition: a survey

Beddiar

Nini

Sabokrou

et al. 2020

Multimed Tools Appl

330

117

View full text Add to dashboard Cite

show abstract

“…Ren et al (2016) demonstrated that ensemble methods can provide more accurate predictions than using the Softmax layers. Chu and Chu (2019) integrated genetic programming with CNN features for the image classification task. Niu and Suen (2012) proposed a novel combined CNN-SVM approach for the handwritten recognition task by using features extracted from CNN and SVM as a classifier.…”

Section: Introductionmentioning

confidence: 99%

Concrete crack detection and 3D mapping by integrated convolutional neural networks architecture

Chaiyasarn

Buatik

Likitlersuang

2020

Advances in Structural Engineering

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This paper presents an image-based crack detection system, in which its architecture is modified to use deep convolutional neural networks in a feature extraction step and other classifiers in the classification step. In the classification step, classifiers including Support Vector machines (SVMs), Random Forest (RF) and Evolutionary Artificial Neural Network (EANN) are used as an alternative to a Softmax classifier and the performance of these classifiers are studied. The data set was created from various types of concrete structures using a standard digital camera and an unmanned aerial vehicle (UAV). The collected images are used in the crack detection system and in creating a 3D model of a sample concrete building using an image- based 3D photogrammetry technique. Then, the 3D model is used to create a mosaic image, in which the crack detection system was applied to create a global view of a crack density map. The map is then projected onto the 3D model to allow cracks to be located in the 3D world. A comparative study was conducted on the proposed crack detection system and the results prove that the combined architecture of CNN as a feature extractor and SVM as a classifier shows the best performance with the accuracy of 92.80. The results also show that the modified architecture by integrating CNN and other types of classifiers can improve a system performance, which is better than using the Softmax classifier.

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Performance Improvement Of Pre-trained Convolutional Neural Networks For Action Recognition

Ozcan

Baştürk

2020

The Computer Journal

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Action recognition is a challenging task. Deep learning models have been investigated to solve this problem. Setting up a new neural network model is a crucial and time-consuming process. Alternatively, pre-trained convolutional neural network (CNN) models offer rapid modeling. The selection of the hyperparameters of CNNs is a challenging issue that heavily depends on user experience. The parameters of CNNs should be carefully selected to get effective results. For this purpose, the artificial bee colony (ABC) algorithm is used for tuning the parameters to get optimum results. The proposed method includes three main stages: the image preprocessing stage involves automatic cropping of the meaningful area within the images in the data set, the transfer learning stage includes experiments with six different pre-trained CNN models and the hyperparameter tuning stage using the ABC algorithm. Performance comparison of the pre-trained CNN models involving the use and nonuse of the ABC algorithm for the Stanford 40 data set is presented. The experiments show that the pre-trained CNN models with ABC are more successful than pre-trained CNN models without ABC. Additionally, to the best of our knowledge, the improved NASNet-Large CNN model with the ABC algorithm gives the best accuracy of 87.78% for the overall success rate-based performance metric.

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A Genetic Programming Approach to Integrate Multilayer CNN Features for Image Classification

Cited by 3 publications

References 11 publications

Vision-based human activity recognition: a survey

Vision-based human activity recognition: a survey

Concrete crack detection and 3D mapping by integrated convolutional neural networks architecture

Performance Improvement Of Pre-trained Convolutional Neural Networks For Action Recognition

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