Affective emotion classification using feature vector of image based on visual concepts

Priya, D. Tamil; Udayan, J. Divya

doi:10.1177/0020720920936834

Cited by 8 publications

(6 citation statements)

References 36 publications

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“…6 At the same time, with the development of recognition algorithm, it has become a trend to apply lightweight network to target detection. Deep neural network model has been widely used in computer vision tasks such as image classification, 7 object detection 8 and target tracking. 9 However, with the deepening of researches, the volume of neural network is becoming larger and larger; the structure is more and more complex, then the hardware resources needed for prediction and training are gradually increasing.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Recognition of students’ behavior states in classroom based on improved MobileNetV2 algorithm

Cao

Liu

Hao

et al. 2021

International Journal of Electrical Engineering & Education

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Analyzing and learning students' behavior states in classroom plays a positive role in understanding and improving the teaching effectiveness. Meanwhile, the application of lightweight network to pattern recognition has become a trend with the development of mobile networks. In order to improve the recognition accuracy of the lightweight network model MobileNetV2 and reduce the computational cost and delay caused by extracting rich features, an improved lightweight network model based on MobileNetV2 is proposed, in which an improved reverse residual module (C-Inverted residual block) is applied to replace the traditional module. In the improved reverse residual module, channel split operation is added to reduce MAC, and channel shuffle operations are used to promote information exchange and channel fusion. Experiments were carried out on Pascal VOC 2007 detection data set to test the general performance of the proposed improved model. Under the operation limits of 140 MFLOPS, 40 MFLOPS and 20 MFLOPS, mean average precision (mAP) of the improved MobileNetV2 algorithm increased by 1.2%, 2.2% and 4.3% compared with MobileNetV2. While the recognition accuracy of the proposed network model on self-made dataset of student classroom behavior states is 4.6% and 3.7% higher than that of MobileNetV1 and MobileNetV2 respectively, and the average recognition rate of students' classroom behavior states can be up to 92.7%. The results of this research combined with mobile networks would be expected to be used to evaluate teaching and learning effects and promote teaching quality improvement.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Recognition of students’ behavior states in classroom based on improved MobileNetV2 algorithm

Cao

Liu

Hao

et al. 2021

International Journal of Electrical Engineering & Education

View full text Add to dashboard Cite

show abstract

“…Yao et al [17] conducted polarity and emotion-specific attention on the lower layers and higher layers, respectively. Priya et al [18] combines the extracted high-level features and low-level features with equal weight. Qu et al [19] proposed a multi-level context pyramid network (MCPNet) for visual sentiment analysis by combining local and global representations.…”

Section: Emotional Region Predictionmentioning

confidence: 99%

Image Emotion Classification Network Based on Multilayer Attentional Interaction, Adaptive Feature Aggregation

Zhang¹,

Yuan²,

Sun³

et al. 2023

Computers, Materials &Amp; Continua

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The image emotion classification task aims to use the model to automatically predict the emotional response of people when they see the image. Studies have shown that certain local regions are more likely to inspire an emotional response than the whole image. However, existing methods perform poorly in predicting the details of emotional regions and are prone to overfitting during training due to the small size of the dataset. Therefore, this study proposes an image emotion classification network based on multilayer attentional interaction and adaptive feature aggregation. To perform more accurate emotional region prediction, this study designs a multilayer attentional interaction module. The module calculates spatial attention maps for higher-layer semantic features and fusion features through a multilayer shuffle attention module. Through layer-by-layer up-sampling and gating operations, the higher-layer features guide the lower-layer features to learn, eventually achieving sentiment region prediction at the optimal scale. To complement the important information lost by layer-by-layer fusion, this study not only adds an intra-layer fusion to the multilayer attention interaction module but also designs an adaptive feature aggregation module. The module uses global average pooling to compress spatial information and connect channel information from all layers. Then, the module adaptively generates a set of aggregated weights through two fully connected layers to augment the original features of each layer. Eventually, the semantics and details of the different layers are aggregated through gating operations and residual connectivity to complement the lost information. To reduce overfitting on small datasets, the network is pre-trained on the FI dataset, and further weight fine-tuning is performed on the small dataset. The experimental results on the FI, Twitter I and Emotion ROI (Region of Interest) datasets show that the proposed network exceeds existing image emotion classification methods, with accuracies of 90.27%, 84.66% and 84.96%.

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“…This is true regardless of the medium, whether they are abstract images, artistic images, or digital photographs. These facts have lead to some researchers integrating both object information and image background (Kim, Kim, Kim, & Lee, 2017;Priya & Udayan, 2020). Although the color is a low-level characteristic, it has a relevant relationship with both the object and the meaning expressed in the image (González-Martín et al, 2022).…”

Section: Recognizing Emotions: Affective Computationmentioning

confidence: 99%

Detection of Emotions in Artworks Using a Convolutional Neural Network Trained on Non-Artistic Images: A Methodology to Reduce the Cross-Depiction Problem

González-Martín

Carrasco

Wielandt

2023

Empirical Studies of the Arts

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This research is framed within the study of automatic recognition of emotions in artworks, proposing a methodology to improve performance in detecting emotions when a network is trained with an image type different from the entry type, which is known as the cross-depiction problem. To achieve this, we used the QuickShift algorithm, which simplifies images’ resources, and applied it to the Open Affective Standardized Image (OASIS) dataset as well as the WikiArt Emotion dataset. Both datasets are also unified under a binary emotional system. Subsequently, a model was trained based on a convolutional neural network using OASIS as a learning base, in order to then be applied on the WikiArt Emotion dataset. The results show an improvement in the general prediction performance when applying QuickShift (73% overall). However, we can observe that artistic style influences the results, with minimalist art being incompatible with the methodology proposed.

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Affective emotion classification using feature vector of image based on visual concepts

Cited by 8 publications

References 36 publications

RETRACTED: Recognition of students’ behavior states in classroom based on improved MobileNetV2 algorithm

RETRACTED: Recognition of students’ behavior states in classroom based on improved MobileNetV2 algorithm

Image Emotion Classification Network Based on Multilayer Attentional Interaction, Adaptive Feature Aggregation

Detection of Emotions in Artworks Using a Convolutional Neural Network Trained on Non-Artistic Images: A Methodology to Reduce the Cross-Depiction Problem

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