Multi Agent-Learner based Online Feature Selection system

Saïd, F.; Alimi, Adel M.

doi:10.1109/smc.2016.7844801

Cited by 3 publications

(2 citation statements)

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“…This sparse method has been accurate enough to create a weight vector for binary classification. The same fraction of the selected features (10% of all dimensions and the rest of the weights were zeros) used by [16,17,18] were chosen for use. BenSaid et al…”

Section: Proposed Training Methodology Of Cnnafdmentioning

confidence: 99%

“…The gradient features could thus be suited for animal face detection in images. The Automated Negotiation-based Online Feature Selection (ANOFS) is a sparse online learning method introduced by BenSaid and Alimi [16,17,18]. The aim of this method is to select a small number of features for binary classification on small databases and thereby replace the traditional optimizers (such as SGDM and ADAM) by the ANOFS, which decreases the number of layer parameters and operations.…”

Section: Cnnafd: Convolutional Neural Network For Animal Face Detectionmentioning

confidence: 99%

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A New Convolutional Neural Network based on a Saprse Convolutional Layer for Animal Face Detection

Jarraya¹,

BenSaid²,

Ouarda³

et al. 2021

Preprint

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This paper focuses on the face detection problem of three popular animal cat-egories that need control such as horses, cats and dogs. To be precise, a new Convolutional Neural Network for Animal Face Detection (CNNAFD) is actu-ally investigated using processed filters based on gradient features and applied with a new way. A new convolutional layer is proposed through a sparse feature selection method known as Automated Negotiation-based Online Feature Selection (ANOFS). CNNAFD ends by stacked fully connected layers which represent a strong classifier. The fusion of CNNAFD and MobileNetV2 constructs the newnetwork CNNAFD-MobileNetV2 which improves the classification results and gives better detection decisions. Our work also introduces a new Tunisian Horse Detection Database (THDD). The proposed detector with the new CNNAFD-MobileNetV2 network achieved an average precision equal to 99.78%, 99% and 98.28% for cats, dogs and horses respectively.

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Section: Proposed Training Methodology Of Cnnafdmentioning

confidence: 99%

Section: Cnnafd: Convolutional Neural Network For Animal Face Detectionmentioning

confidence: 99%