Shallow convolutional neural network for image classification

Lei, Fangyuan; Liu, Xun; Dai, Qingyun; Ling, Bingo Wing‐Kuen

doi:10.1007/s42452-019-1903-4

Cited by 56 publications

(34 citation statements)

References 16 publications

(6 reference statements)

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“…Lei et al [25] proposed a shallow deep learning model to classify images. In this network, they used seven layers: CPCPDD (C, P, D denotes convolution, pooling, and dense layers respectively).…”

Section: Literature Studymentioning

confidence: 99%

Movie Title Extraction and Script Separation Using Shallow Convolution Neural Network

et al. 2021

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Graphical texts in natural images play an important role in portraying information in multitudinous fields such as communication, education, and entertainment to name a few. Recognizing text in scene images is challenging due to the inherent complexity of the images. Text recognition in natural images involves script identification, which requires text localization. This is not trivial for natural scene images due to the presence of disparate foreground/background components. For scene images like movie posters, the challenge is more dominant. The challenges aggravate due to the presence of composite characteristics of posters like complex graphics background and the presence of different texts like a movie title, names of actors, producers, directors, and tagline. These texts have miscellaneous fonts, variations in colors, size, orientation, and textures. In this work, an M-EAST (modified EAST) model is proposed, which is based on the EAST (efficient and accurate scene text detector) model for text localization. A novel movie title extraction is thereafter used for separating the title from the extracted text pool. Finally, the title script was identified using a shallow convolutional neural network (SCNN)-based to ensure functionality in low-resource environments. Experiments were performed on a dataset of movieposter images of Tollywood, Bollywood, and Hollywood industries, and a highest accuracy of 99.82% was obtained. The system performed better than the reported techniques.

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Section: Literature Studymentioning

confidence: 99%

Movie Title Extraction and Script Separation Using Shallow Convolution Neural Network

et al. 2021

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“…In this study we decided to use so-called "shallow convolutional neural networks" (SCNNs) which use only a small number of image convolutions and subsequent fullyconnected layers thereby reducing the requirement of high computational resources and training time [11,12]. As proof of concept, the SCNNs were trained to detect DM infections on RGB images of experimentally inoculated leaf discs.…”

Section: Introductionmentioning

confidence: 99%

High-Throughput Phenotyping of Leaf Discs Infected with Grapevine Downy Mildew Using Shallow Convolutional Neural Networks

et al. 2021

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Objective and standardized recording of disease severity in mapping crosses and breeding lines is a crucial step in characterizing resistance traits utilized in breeding programs and to conduct QTL or GWAS studies. Here we report a system for automated high-throughput scoring of disease severity on inoculated leaf discs. As proof of concept, we used leaf discs inoculated with Plasmopara viticola ((Berk. and Curt.) Berl. and de Toni) causing grapevine downy mildew (DM). This oomycete is one of the major grapevine pathogens and has the potential to reduce grape yield dramatically if environmental conditions are favorable. Breeding of DM resistant grapevine cultivars is an approach for a novel and more sustainable viticulture. This involves the evaluation of several thousand inoculated leaf discs from mapping crosses and breeding lines every year. Therefore, we trained a shallow convolutional neural-network (SCNN) for efficient detection of leaf disc segments showing P. viticola sporangiophores. We could illustrate a high and significant correlation with manually scored disease severity used as ground truth data for evaluation of the SCNN performance. Combined with an automated imaging system, this leaf disc-scoring pipeline has the potential to considerably reduce the amount of time during leaf disc phenotyping. The pipeline with all necessary documentation for adaptation to other pathogens is freely available.

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“…In a further study deep learning approaches using CNNs were employed to detect grapevine downy mildew and spider mites under field conditions on images of whole plants [10]. In this study we decided to use so-called "shallow convolutional neural networks" (SCNNs) which use only a small number of image convolutions and subsequent fully-connected layers thereby reducing the requirement of high computational resources and training time [11,12]. As proof of concept, the SCNNs were trained to detect DM infections on RGB images of experimentally inoculated leaf discs.…”

Section: Introductionmentioning

confidence: 99%

High-throughput phenotyping of leaf discs infected with grapevine downy mildew using shallow convolutional neural networks

Zendler

Malagol

Schwandner

et al. 2021

Preprint

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Objective and standardized recording of disease severity in mapping crosses and breeding lines is a crucial step in characterizing resistance traits utilized in breeding programs and to conduct QTL or GWAS studies. Here we report a system for automated high-throughput scoring of disease severity on inoculated leaf discs. As proof of concept, we used leaf discs inoculated with Plasmopara viticola causing grapevine downy mildew (DM). This oomycete is one of the major grapevine pathogens and has the potential to reduce grape yield dramatically if environmental conditions are favorable. Breeding of DM resistant grapevine cultivars is an approach for a novel and more sustainable viticulture. This involves the evaluation of several thousand inoculated leaf discs from mapping crosses and breeding lines every year. Therefore, we trained a shallow convolutional neural-network (SCNN) for efficient detection of leaf disc segments showing P. viticola sporangiophores. We could illustrate a high and significant correlation with manually scored disease severity used as ground truth data for evaluation of the SCNN performance. Combined with an automated imaging system, this leaf disc-scoring pipeline has the potential to reduce the amount of time during leaf disc phenotyping considerably. The pipeline with all necessary documentation for adaptation to other pathogens is freely available.

show abstract

Shallow convolutional neural network for image classification

Cited by 56 publications

References 16 publications

Movie Title Extraction and Script Separation Using Shallow Convolution Neural Network

Movie Title Extraction and Script Separation Using Shallow Convolution Neural Network

High-Throughput Phenotyping of Leaf Discs Infected with Grapevine Downy Mildew Using Shallow Convolutional Neural Networks

High-throughput phenotyping of leaf discs infected with grapevine downy mildew using shallow convolutional neural networks

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