Research on Image Classification Method Based on Adaboost-DBN

Sun, Hongjian; Tao, Wuchao; Wang, Ran; Ren, Cong; Zhao, Zhijie

doi:10.1007/978-3-030-19156-6_21

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…Human action recognition in videos has been actively investigated. Traditionally, actions have been recognized by extracting feature points from the actions frequently arising in an image, drawing a specific vector, and applying a pattern classifier, such as Support Vector Machine (SVM) [15], AdaBoost [16], or random forest. Obtaining external factors, such as domain knowledge, is necessary to develop an effective model.…”

Section: Technological Trends In the Video Action Classification Modelmentioning

confidence: 99%

Deep Learning-Based Action Classification Using One-Shot Object Detection

Yoo,

Lee,

Chung

2023

Computers, Materials &Amp; Continua

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Deep learning-based action classification technology has been applied to various fields, such as social safety, medical services, and sports. Analyzing an action on a practical level requires tracking multiple human bodies in an image in real-time and simultaneously classifying their actions. There are various related studies on the real-time classification of actions in an image. However, existing deep learning-based action classification models have prolonged response speeds, so there is a limit to real-time analysis. In addition, it has low accuracy of action of each object if multiple objects appear in the image. Also, it needs to be improved since it has a memory overhead in processing image data. Deep learning-based action classification using one-shot object detection is proposed to overcome the limitations of multiframe-based analysis technology. The proposed method uses a one-shot object detection model and a multi-object tracking algorithm to detect and track multiple objects in the image. Then, a deep learning-based pattern classification model is used to classify the body action of the object in the image by reducing the data for each object to an action vector. Compared to the existing studies, the constructed model shows higher accuracy of 74.95%, and in terms of speed, it offered better performance than the current studies at 0.234 s per frame. The proposed model makes it possible to classify some actions only through action vector learning without additional image learning because of the vector learning feature of the posterior neural network. Therefore, it is expected to contribute significantly to commercializing realistic streaming data analysis technologies, such as CCTV.

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Section: Technological Trends In the Video Action Classification Modelmentioning

confidence: 99%

Deep Learning-Based Action Classification Using One-Shot Object Detection

Yoo,

Lee,

Chung

2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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Performance Evaluation of Deep Learning Classification Network for Image Features

Yang

Guo

et al. 2021

IEEE Access

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This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Letters on IEEE Xplore. In recent years, deep learning has been widely used in SAR ATR and achieved excellent performance on the MSTAR dataset. However, due to constrained imaging conditions, MSTAR has data biases such as background correlation, i.e., background clutter properties have a spurious correlation with target classes. Deep learning can overfit clutter to reduce training errors. Therefore, the degree of overfitting for clutter reflects the non-causality of deep learning in SAR ATR. Existing methods only qualitatively analyze this phenomenon. In this paper, we quantify the contributions of different regions to target recognition based on the Shapley value. The Shapley value of clutter measures the degree of overfitting. Moreover, we explain how data bias and model bias contribute to noncausality. Concisely, data bias leads to comparable signal-toclutter ratios and clutter textures in training and test sets. And various model structures have different degrees of overfitting for these biases. The experimental results of various models under standard operating conditions on the MSTAR dataset support our conclusions. Index Terms-Synthetic aperture radar (SAR), automatic target recognition (ATR), deep learning, data bias, Shapley value, causality 2 Texture refers to the spatial organization of a set of basic elements [7]. As shown in Fig. 1, the scattering point amplitude has different spatial distributions in the target, clutter, and shadow regions. These spatial distributions create texture signatures of different regions in SAR images.

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Research on Image Classification Method Based on Adaboost-DBN

Cited by 2 publications

References 8 publications

Deep Learning-Based Action Classification Using One-Shot Object Detection

Deep Learning-Based Action Classification Using One-Shot Object Detection

Performance Evaluation of Deep Learning Classification Network for Image Features

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