Image annotation for Mexican buildings database

Obeso, Abraham Montoya; Reyes, Laura Mariel Amaya; Rodríguez, Mario López; Cruz, Mario Humberto Mijes; Vázquez, Mireya Saraí García; Benois-Pineau, Jenny; Fuentes, Luis Miguel Zamudio; Martínez, Elizabeth Cano; Secundino, Jesús Abimelek Flores; Martínez, José F.; Acosta, Alejandro

doi:10.1117/12.2238352

Cited by 5 publications

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“…Each of the feature maps in layer k is thresholded based on the K-sigma rule to create binary maps B k ða i;j;k Þ, where i; j denote the spatial dimension of the k feature maps as shown in Eq. (26). The mean μ k and standard deviation σ k are calculated for each of the k feature maps followed by the thresholding to create k binary maps ðB k Þ. K is the parameter that controls the threshold value and is set to 1 by the authors in their work: E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 2 6 ; 1 1 6 ; 3 4 9…”

Section: Feature-based Explanation Methodsmentioning

confidence: 99%

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Review of white box methods for explanations of convolutional neural networks in image classification tasks

2021

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In recent years, deep learning has become prevalent to solve applications from multiple domains. Convolutional neural networks (CNNs) particularly have demonstrated state-ofthe-art performance for the task of image classification. However, the decisions made by these networks are not transparent and cannot be directly interpreted by a human. Several approaches have been proposed to explain the reasoning behind a prediction made by a network. We propose a topology of grouping these methods based on their assumptions and implementations. We focus primarily on white box methods that leverage the information of the internal architecture of a network to explain its decision. Given the task of image classification and a trained CNN, our work aims to provide a comprehensive and detailed overview of a set of methods that can be used to create explanation maps for a particular image, which assign an importance score to each pixel of the image based on its contribution to the decision of the network. We also propose a further classification of the white box methods based on their implementations to enable better comparisons and help researchers find methods best suited for different scenarios.

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Section: Feature-based Explanation Methodsmentioning

confidence: 99%

“…Fig.7Samples showing the saliency maps for the (a) sample image, (b) gradient backpropagation, and (c) guided backpropagation methods. Sample taken from MexCulture Architectural Styles dataset 26. …”

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confidence: 99%