Convolutional Neural Networks in the Task of Image Classification

Zelenina, Larisa I.; Khaimina, Liudmila E.; Khaimin, Evgenii S.; Khripunov, D.S.; Zashikhina, Inga

doi:10.53656/math2022-1-2-con

Cited by 5 publications

(5 citation statements)

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“…Initially, action recognition based on skeletal data also tried to use manual feature extraction methods, and with the popularity of deep learning algorithms, automatic skeletal data feature extraction methods based on deep learning algorithms were proposed one after another. *e commonly used approaches for modeling skeletal data can be classified as Recurrent Neural Network (RNN) based [16], CNN based [17], Graph Convolutional Network (GCN) based [18], etc. Reference [19] uses Long Short-Term Memory (LSTM) to mine the information in the data.…”

Section: Introductionmentioning

confidence: 99%

A Multimodal Information Fusion Model for Robot Action Recognition with Time Series

Zhang¹,

Li²,

Qian³

2022

Journal of Electrical and Computer Engineering

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The current robotics field, led by a new generation of information technology, is moving into a new stage of human-machine collaborative operation. Unlike traditional robots that need to use isolation rails to maintain a certain safety distance from people, the new generation of human-machine collaboration systems can work side by side with humans without spatial obstruction, giving full play to the expertise of people and machines through an intelligent assignment of operational tasks and improving work patterns to achieve increased efficiency. The robot’s efficient and accurate recognition of human movements has become a key factor in measuring robot performance. Usually, the data for action recognition is video data, and video data is time-series data. Time series describe the response results of a certain system at different times. Therefore, the study of time series can be used to recognize the structural characteristics of the system and reveal its operation law. As a result, this paper proposes a time series-based action recognition model with multimodal information fusion and applies it to a robot to realize friendly human-robot interaction. Multifeatures can characterize data information comprehensively, and in this study, the spatial flow and motion flow features of the dataset are extracted separately, and each feature is input into a bidirectional long and short-term memory network (BiLSTM). A confidence fusion method was used to obtain the final action recognition results. Experiment results on the publicly available datasets NTU-RGB + D and MSR Action 3D show that the method proposed in this paper can improve action recognition accuracy.

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Section: Introductionmentioning

confidence: 99%

A Multimodal Information Fusion Model for Robot Action Recognition with Time Series

Zhang¹,

Li²,

Qian³

2022

Journal of Electrical and Computer Engineering

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“…Their inherent ability to effectively capture and extract meaningful features from images has contributed to their widespread adoption and success in various visual recognition tasks. The utilization of CNNs has propelled advancements in the field of computer vision, paving the way for enhanced capabilities and improved performance in tasks that require sophisticated understanding and interpretation of visual data ( Maggiori et al., 2017 ; Ashraf et al., 2019 ; Hui et al., 2020 ; Chen et al., 2021 ; Zelenina et al., 2022 ).…”

Section: Related Workmentioning

confidence: 99%

Deep learning for Chilean native flora classification: a comparative analysis

Figueroa-Flores,

San-Martin

2023

Front. Plant Sci.

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The limited availability of information on Chilean native flora has resulted in a lack of knowledge among the general public, and the classification of these plants poses challenges without extensive expertise. This study evaluates the performance of several Deep Learning (DL) models, namely InceptionV3, VGG19, ResNet152, and MobileNetV2, in classifying images representing Chilean native flora. The models are pre-trained on Imagenet. A dataset containing 500 images for each of the 10 classes of native flowers in Chile was curated, resulting in a total of 5000 images. The DL models were applied to this dataset, and their performance was compared based on accuracy and other relevant metrics. The findings highlight the potential of DL models to accurately classify images of Chilean native flora. The results contribute to enhancing the understanding of these plant species and fostering awareness among the general public. Further improvements and applications of DL in ecology and biodiversity research are discussed.

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“…These video clips are segmented into seven labels, happy, sad, neutral, angry, surprisde, disgusted and fearful. Convolutional neural network (CNN) is widely used for classification [4]. By using CNN to classify the large scaled dataset, the model is hard to be overfitted and the efficiency will not be low [4].…”

Section: Introductionmentioning

confidence: 99%

“…Convolutional neural network (CNN) is widely used for classification [4]. By using CNN to classify the large scaled dataset, the model is hard to be overfitted and the efficiency will not be low [4].…”

Section: Introductionmentioning

confidence: 99%

Machine learning and emotion recognition in video clips

Chang

2023

ACE

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The development and application of machine learning has been a hot topic in recent years, and enabling machines to read people's emotions is of great importance in many aspects of social development, such as healthcare, products development and human-computer interaction. This study recognizes seven human emotions by building a model of a neural network and training dataset. Using a large-scaled video dataset, Dynamic Facial Expression in-the-Wild (DFEW), trains the ResNet50 model and outputs the precisions. There are three possible training results, if the accuracies are all above 80%, then the neural network is well-performed; if partial classification accuracies is above 80% while others are above 50%, the neural network is partial well-performed; if the accuracies are all under 50%, the neural network is poor-performed.

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Convolutional Neural Networks in the Task of Image Classification

Cited by 5 publications

References 0 publications

A Multimodal Information Fusion Model for Robot Action Recognition with Time Series

A Multimodal Information Fusion Model for Robot Action Recognition with Time Series

Deep learning for Chilean native flora classification: a comparative analysis

Machine learning and emotion recognition in video clips

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